def run_TPI(income_tax_params,
tpi_params,
iterative_params,
initial_values,
SS_values,
output_dir="./OUTPUT"):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, e, retire, mean_income_data,\
factor, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n = tpi_params
K0, b_sinit, b_splus1init, L0, Y0,\
w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n = initial_values
Kss, Lss, rss, wss, BQss, T_Hss, bssmat_splus1, nssmat = SS_values
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
domain = np.linspace(0, T, T)
K_init = (-1 / (domain + 1)) * (Kss - K0) + Kss
K_init[-1] = Kss
K_init = np.array(list(K_init) + list(np.ones(S) * Kss))
L_init = np.ones(T + S) * Lss
K = K_init
L = L_init
Y_params = (alpha, Z)
Y = firm.get_Y(K, L, Y_params)
w = firm.get_w(Y, L, alpha)
r_params = (alpha, delta)
r = firm.get_r(Y, K, r_params)
BQ = np.zeros((T + S, J))
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
if T_Hss < 1e-13 and T_Hss > 0.0:
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2
# Make array of initial guesses for labor supply and savings
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
K_plot = list(K) + list(np.ones(10) * Kss)
L_plot = list(L) + list(np.ones(10) * Lss)
plt.figure()
plt.axhline(y=Kss,
color='black',
linewidth=2,
label=r"Steady State $\hat{K}$",
ls='--')
plt.plot(np.arange(T + 10),
Kpath_plot[:T + 10],
'b',
linewidth=2,
label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
# theta_params = (e[-1, j], 1, omega[0].reshape(S, 1), lambdas[j])
# theta = tax.replacement_rate_vals(n, w, factor, theta_params)
theta = np.zeros((J, ))
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values,
theta, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars,
inner_loop_params)
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J),
g_n_vector[:T], 'TPI')
K[:T] = household.get_K(b_mat[:T], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
wnew = firm.get_w(Ynew[:T], L[:T], alpha)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
BQ_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J),
rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat[:T, :, :],
BQ_params)
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[1:T + 1, :, :]
TH_tax_params = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
TH_tax_params[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
T_H_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)),
lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1),
'TPI', TH_tax_params, theta, tau_bq, tau_payroll,
h_wealth, p_wealth, m_wealth, retire, T, S, J)
T_H_new = np.array(
list(
tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1), (
1, S, J)), np.tile(wnew[:T].reshape(T, 1, 1), (
1, S, J)), bmat_s, n_mat[:T, :, :], BQnew[:T].reshape(
T, 1, J), factor, T_H_params)) + [T_Hss] * S)
w[:T] = utils.convex_combo(wnew[:T], w[:T], nu)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
if T_H.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(utils.pct_diff_func(wnew[:T], w[:T])) +
list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(utils.pct_diff_func(wnew[:T], w[:T])) +
list(np.abs(T_H_new[:T], T_H[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
if ((TPIiter >= maxiter) or
(np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS:
raise RuntimeError("Transition path equlibrium not found")
Y[:T] = Ynew
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, theta,
ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars,
inner_loop_params)
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J),
g_n_vector[:T], 'TPI')
K[:T] = household.get_K(
b_mat[:T],
K_params) # this is what old code does, but it's strange - why use
# b_mat -- what is going on with initial period, etc.
etr_params_path = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
tax_path_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), lambdas, 'TPI',
retire, etr_params_path, h_wealth, p_wealth, m_wealth,
tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1), (1, S, J)),
np.tile(w[:T].reshape(T, 1, 1),
(1, S, J)), bmat_s, n_mat[:T, :, :],
BQ[:T, :].reshape(T, 1, J), factor,
T_H[:T].reshape(T, 1, 1), None, False,
tax_path_params)
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1),
bmat_s, bmat_splus1, n_mat[:T, :, :],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
I_params = (delta, g_y, g_n_vector[:T])
I = firm.get_I(K[1:T + 1], K[:T], I_params)
print 'Resource Constraint Difference:', Y[:T] - C[:T] - I[:T]
print 'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_path[t], t,
ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found")
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y,
'K': K,
'L': L,
'C': C,
'I': I,
'BQ': BQ,
'T_H': T_H,
'r': r,
'w': w,
'b_mat': b_mat,
'n_mat': n_mat,
'c_path': c_path,
'tax_path': tax_path,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure
}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
macro_output = {
'Y': Y,
'K': K,
'L': L,
'C': C,
'I': I,
'BQ': BQ,
'T_H': T_H,
'r': r,
'w': w,
'tax_path': tax_path
}
# Non-stationary output
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
return output, macro_output
def SS_solver(b_guess_init, n_guess_init, rss, wss, T_Hss, factor_ss, Yss, params, baseline, fsolve_flag=False, baseline_spending=False):
'''
--------------------------------------------------------------------
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using a bisection method similar to TPI.
--------------------------------------------------------------------
INPUTS:
b_guess_init = [S,J] array, initial guesses for savings
n_guess_init = [S,J] array, initial guesses for labor supply
wguess = scalar, initial guess for SS real wage rate
rguess = scalar, initial guess for SS real interest rate
T_Hguess = scalar, initial guess for lump sum transfer
factorguess = scalar, initial guess for scaling factor to dollars
chi_b = [J,] vector, chi^b_j, the utility weight on bequests
chi_n = [S,] vector, chi^n_s utility weight on labor supply
params = length X tuple, list of parameters
iterative_params = length X tuple, list of parameters that determine the convergence
of the while loop
tau_bq = [J,] vector, bequest tax rate
rho = [S,] vector, mortality rates by age
lambdas = [J,] vector, fraction of population with each ability type
omega = [S,] vector, stationary population weights
e = [S,J] array, effective labor units by age and ability type
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
euler_equation_solver()
household.get_K()
firm.get_L()
firm.get_Y()
firm.get_r()
firm.get_w()
household.get_BQ()
tax.replacement_rate_vals()
tax.revenue()
utils.convex_combo()
utils.pct_diff_func()
OBJECTS CREATED WITHIN FUNCTION:
b_guess = [S,] vector, initial guess at household savings
n_guess = [S,] vector, initial guess at household labor supply
b_s = [S,] vector, wealth enter period with
b_splus1 = [S,] vector, household savings
b_splus2 = [S,] vector, household savings one period ahead
BQ = scalar, aggregate bequests to lifetime income group
theta = scalar, replacement rate for social security benenfits
error1 = [S,] vector, errors from FOC for savings
error2 = [S,] vector, errors from FOC for labor supply
tax1 = [S,] vector, total income taxes paid
cons = [S,] vector, household consumption
OBJECTS CREATED WITHIN FUNCTION - SMALL OPEN ONLY
Bss = scalar, aggregate household wealth in the steady state
BIss = scalar, aggregate household net investment in the steady state
RETURNS: solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
OUTPUT: None
--------------------------------------------------------------------
'''
bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params = params
J, S, T, BW, beta, sigma, alpha, gamma, epsilon, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, tau_bq, rho, omega_SS, budget_balance, \
alpha_T, debt_ratio_ss, tau_b, delta_tau,\
lambdas, imm_rates, e, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
chi_b, chi_n = chi_params
maxiter, mindist_SS = iterative_params
small_open, ss_firm_r, ss_hh_r = small_open_params
# Rename the inputs
r = rss
w = wss
T_H = T_Hss
factor = factor_ss
if budget_balance == False:
if baseline_spending == True:
Y = Yss
else:
Y = T_H / alpha_T
if small_open == True:
r = ss_hh_r
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, Y and
# factor
if budget_balance:
outer_loop_vars = (bssmat, nssmat, r, w, T_H, factor)
else:
outer_loop_vars = (bssmat, nssmat, r, w, Y, T_H, factor)
inner_loop_params = (ss_params, income_tax_params, chi_params, small_open_params)
euler_errors, bssmat, nssmat, new_r, new_w, \
new_T_H, new_Y, new_factor, new_BQ, average_income_model = inner_loop(outer_loop_vars, inner_loop_params, baseline, baseline_spending)
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
if budget_balance:
T_H = utils.convex_combo(new_T_H, T_H, nu)
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_T_H, T_H)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
Y = utils.convex_combo(new_Y, Y, nu)
if Y != 0:
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_Y, Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
# If Y is zero (if there is no output), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[abs(new_Y - Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
print 'New value of nu:', nu
iteration += 1
print "Iteration: %02d" % iteration, " Distance: ", dist
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
bssmat_s = np.append(np.zeros((1,J)),bssmat[:-1,:],axis=0)
bssmat_splus1 = bssmat
rss = r
wss = w
factor_ss = factor
T_Hss = T_H
Lss_params = (e, omega_SS.reshape(S, 1), lambdas, 'SS')
Lss = firm.get_L(nssmat, Lss_params)
if small_open == False:
Kss_params = (omega_SS.reshape(S, 1), lambdas, imm_rates, g_n_ss, 'SS')
Bss = household.get_K(bssmat_splus1, Kss_params)
if budget_balance:
debt_ss = 0.0
else:
debt_ss = debt_ratio_ss*Y
Kss = Bss - debt_ss
Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
Iss = firm.get_I(bssmat_splus1, Kss, Kss, Iss_params)
else:
# Compute capital (K) and wealth (B) separately
Kss_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
Kss = firm.get_K(Lss, ss_firm_r, Kss_params)
Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
InvestmentPlaceholder = np.zeros(bssmat_splus1.shape)
Iss = firm.get_I(InvestmentPlaceholder, Kss, Kss, Iss_params)
Bss_params = (omega_SS.reshape(S, 1), lambdas, imm_rates, g_n_ss, 'SS')
Bss = household.get_K(bssmat_splus1, Bss_params)
BIss_params = (0.0, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
BIss = firm.get_I(bssmat_splus1, Bss, Bss, BIss_params)
if budget_balance:
debt_ss = 0.0
else:
debt_ss = debt_ratio_ss*Y
# Yss_params = (alpha, Z)
Yss_params = (Z, gamma, epsilon)
Yss = firm.get_Y(Kss, Lss, Yss_params)
# Verify that T_Hss = alpha_T*Yss
# transfer_error = T_Hss - alpha_T*Yss
# if np.absolute(transfer_error) > mindist_SS:
# print 'Transfers exceed alpha_T percent of GDP by:', transfer_error
# err = "Transfers do not match correct share of GDP in SS_solver"
# raise RuntimeError(err)
BQss = new_BQ
theta_params = (e, S, retire)
theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, theta_params)
# Next 5 lines pulled out of inner_loop where they are used to calculate tax revenue. Now calculating G to balance gov't budget.
b_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat[:-1, :]))
lump_sum_params = (e, lambdas.reshape(1, J), omega_SS.reshape(S, 1), 'SS', etr_params, theta, tau_bq,
tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
revenue_ss = tax.revenue(new_r, new_w, b_s, nssmat, new_BQ, Yss, Lss, Kss, factor, lump_sum_params)
r_gov_ss = rss
debt_service_ss = r_gov_ss*debt_ratio_ss*Yss
new_borrowing = debt_ratio_ss*Yss*((1+g_n_ss)*np.exp(g_y)-1)
# government spends such that it expands its debt at the same rate as GDP
if budget_balance:
Gss = 0.0
else:
Gss = revenue_ss + new_borrowing - (T_Hss + debt_service_ss)
# solve resource constraint
etr_params_3D = np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1))
mtrx_params_3D = np.tile(np.reshape(mtrx_params,(S,1,mtrx_params.shape[1])),(1,J,1))
'''
------------------------------------------------------------------------
The code below is to calulate and save model MTRs
- only exists to help debug
------------------------------------------------------------------------
'''
# etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
# etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
# mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
# mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
# e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
# nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
# mtry_ss_params = (e_extended[1:,:], etr_params_extended_3D, mtry_params_extended_3D, analytical_mtrs)
# mtry_ss = tax.MTR_capital(rss, wss, bssmat_splus1, nss_extended[1:,:], factor_ss, mtry_ss_params)
# mtrx_ss_params = (e, etr_params_3D, mtrx_params_3D, analytical_mtrs)
# mtrx_ss = tax.MTR_labor(rss, wss, bssmat_s, nssmat, factor_ss, mtrx_ss_params)
# np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
# np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
# solve resource constraint
taxss_params = (e, lambdas, 'SS', retire, etr_params_3D,
h_wealth, p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
taxss = tax.total_taxes(rss, wss, bssmat_s, nssmat, BQss, factor_ss, T_Hss, None, False, taxss_params)
css_params = (e, lambdas.reshape(1, J), g_y)
cssmat = household.get_cons(rss, wss, bssmat_s, bssmat_splus1, nssmat, BQss.reshape(
1, J), taxss, css_params)
biz_params = (tau_b, delta_tau)
business_revenue = tax.get_biz_tax(wss, Yss, Lss, Kss, biz_params)
Css_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css = household.get_C(cssmat, Css_params)
if small_open == False:
resource_constraint = Yss - (Css + Iss + Gss)
print 'Yss= ', Yss, '\n', 'Gss= ', Gss, '\n', 'Css= ', Css, '\n', 'Kss = ', Kss, '\n', 'Iss = ', Iss, '\n', 'Lss = ', Lss, '\n', 'Debt service = ', debt_service_ss
print 'D/Y:', debt_ss/Yss, 'T/Y:', T_Hss/Yss, 'G/Y:', Gss/Yss, 'Rev/Y:', revenue_ss/Yss, 'business rev/Y: ', business_revenue/Yss, 'Int payments to GDP:', (rss*debt_ss)/Yss
print 'Check SS budget: ', Gss - (np.exp(g_y)*(1+g_n_ss)-1-rss)*debt_ss - revenue_ss + T_Hss
print 'resource constraint: ', resource_constraint
else:
# include term for current account
resource_constraint = Yss + new_borrowing - (Css + BIss + Gss) + (ss_hh_r * Bss - (delta + ss_firm_r) * Kss - debt_service_ss)
print 'Yss= ', Yss, '\n', 'Css= ', Css, '\n', 'Bss = ', Bss, '\n', 'BIss = ', BIss, '\n', 'Kss = ', Kss, '\n', 'Iss = ', Iss, '\n', 'Lss = ', Lss, '\n', 'T_H = ', T_H,'\n', 'Gss= ', Gss
print 'D/Y:', debt_ss/Yss, 'T/Y:', T_Hss/Yss, 'G/Y:', Gss/Yss, 'Rev/Y:', revenue_ss/Yss, 'Int payments to GDP:', (rss*debt_ss)/Yss
print 'resource constraint: ', resource_constraint
if Gss < 0:
print 'Steady state government spending is negative to satisfy budget'
if ENFORCE_SOLUTION_CHECKS and np.absolute(resource_constraint) > mindist_SS:
print 'Resource Constraint Difference:', resource_constraint
err = "Steady state aggregate resource constraint not satisfied"
raise RuntimeError(err)
# check constraints
household.constraint_checker_SS(bssmat, nssmat, cssmat, ltilde)
euler_savings = euler_errors[:S,:]
euler_labor_leisure = euler_errors[S:,:]
'''
------------------------------------------------------------------------
Return dictionary of SS results
------------------------------------------------------------------------
'''
output = {'Kss': Kss, 'bssmat': bssmat, 'Bss': Bss, 'Lss': Lss, 'Css':Css, 'Iss':Iss, 'nssmat': nssmat, 'Yss': Yss,
'wss': wss, 'rss': rss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss,
'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'Gss': Gss, 'revenue_ss': revenue_ss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b}
return output
def run_time_path_iteration(Kss, Lss, Yss, BQss, theta, parameters, g_n_vector, omega_stationary, K0, b_sinit, b_splus1init, L0, Y0, r0, BQ0, T_H_0, tax0, c0, initial_b, initial_n, factor_ss, tau_bq, chi_b, chi_n, get_baseline=False, output_dir="./OUTPUT", **kwargs):
TPI_FIG_DIR = output_dir
# Initialize Time paths
domain = np.linspace(0, T, T)
Kinit = (-1 / (domain + 1)) * (Kss - K0) + Kss
Kinit[-1] = Kss
Kinit = np.array(list(Kinit) + list(np.ones(S) * Kss))
Linit = np.ones(T + S) * Lss
Yinit = firm.get_Y(Kinit, Linit, parameters)
winit = firm.get_w(Yinit, Linit, parameters)
rinit = firm.get_r(Yinit, Kinit, parameters)
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQinit = np.array(BQinit)
T_H_init = np.ones(T + S) * T_Hss
# Make array of initial guesses
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
Kpath_TPI = list(Kinit) + list(np.ones(10) * Kss)
Lpath_TPI = list(Linit) + list(np.ones(10) * Lss)
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), Kpath_TPI[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq), xtol=1e-13))
# if np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1, j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq)).max() > 1e-6:
# print 'minidoughnut:',
# np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1,
# j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b,
# factor_ss, j, parameters, theta, tau_bq)).max()
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(
guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
# if abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n))).max() > 1e-6:
# print 's-loop:',
# abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit,
# BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters,
# theta, tau_bq, rho, lambdas, e, initial_b, chi_b,
# chi_n))).max()
for t in xrange(0, T):
b_guesses_to_use = .75 * \
np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
Kinit = household.get_K(b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T], omega_stationary[
:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
Ynew = firm.get_Y(Kinit, Linit, parameters)
wnew = firm.get_w(Ynew, Linit, parameters)
rnew = firm.get_r(Ynew, Kinit, parameters)
# the following needs a g_n term
BQnew = household.get_BQ(rnew.reshape(T, 1), b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
T_H_new = np.array(list(tax.get_lump_sum(rnew.reshape(T, 1, 1), bmat_s, wnew.reshape(
T, 1, 1), e.reshape(1, S, J), n_mat[:T], BQnew.reshape(T, 1, J), lambdas.reshape(
1, 1, J), factor_ss, omega_stationary[:T].reshape(T, S, 1), 'TPI', parameters, theta, tau_bq)) + [T_Hss] * S)
winit[:T] = utils.convex_combo(wnew, winit[:T], parameters)
rinit[:T] = utils.convex_combo(rnew, rinit[:T], parameters)
BQinit[:T] = utils.convex_combo(BQnew, BQinit[:T], parameters)
T_H_init[:T] = utils.convex_combo(
T_H_new[:T], T_H_init[:T], parameters)
guesses_b = utils.convex_combo(b_mat, guesses_b, parameters)
guesses_n = utils.convex_combo(n_mat, guesses_n, parameters)
if T_H_init.all() != 0:
TPIdist = np.array(list(utils.perc_dif_func(rnew, rinit[:T])) + list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) + list(
utils.perc_dif_func(wnew, winit[:T])) + list(utils.perc_dif_func(T_H_new, T_H_init))).max()
else:
TPIdist = np.array(list(utils.perc_dif_func(rnew, rinit[:T])) + list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) + list(
utils.perc_dif_func(wnew, winit[:T])) + list(np.abs(T_H_new, T_H_init))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
if TPIiter > 10:
if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
nu /= 2
print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
print 'Computing final solutions'
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq), xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
tax_path = tax.total_taxes(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T, :].reshape(T, 1, J), lambdas, factor_ss, T_H_init[:T].reshape(T, 1, 1), None, 'TPI', False, parameters, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T].reshape(T, 1, J), lambdas.reshape(1, 1, J), b_splus1, parameters, tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[
:T].reshape(T, S, 1), lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1],
Kpath_TPI[:T], delta, g_y, g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, parameters)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Kpath_TPI': Kpath_TPI, 'b_mat': b_mat, 'c_path': c_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI, 'n_mat': n_mat,
'rinit': rinit, 'Yinit': Yinit, 'T_H_init': T_H_init,
'tax_path': tax_path, 'winit': winit}
if get_baseline:
tpi_init_dir = os.path.join(output_dir, "TPIinit")
utils.mkdirs(tpi_init_dir)
tpi_init_vars = os.path.join(tpi_init_dir, "TPIinit_vars.pkl")
pickle.dump(output, open(tpi_init_vars, "wb"))
else:
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
def SS_solver(b_guess_init,
n_guess_init,
rss,
wss,
T_Hss,
factor_ss,
Yss,
params,
baseline,
fsolve_flag=False,
baseline_spending=False):
'''
--------------------------------------------------------------------
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using a bisection method similar to TPI.
--------------------------------------------------------------------
INPUTS:
b_guess_init = [S,J] array, initial guesses for savings
n_guess_init = [S,J] array, initial guesses for labor supply
wguess = scalar, initial guess for SS real wage rate
rguess = scalar, initial guess for SS real interest rate
T_Hguess = scalar, initial guess for lump sum transfer
factorguess = scalar, initial guess for scaling factor to dollars
chi_b = [J,] vector, chi^b_j, the utility weight on bequests
chi_n = [S,] vector, chi^n_s utility weight on labor supply
params = length X tuple, list of parameters
iterative_params = length X tuple, list of parameters that determine the convergence
of the while loop
tau_bq = [J,] vector, bequest tax rate
rho = [S,] vector, mortality rates by age
lambdas = [J,] vector, fraction of population with each ability type
omega = [S,] vector, stationary population weights
e = [S,J] array, effective labor units by age and ability type
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
euler_equation_solver()
household.get_K()
firm.get_L()
firm.get_Y()
firm.get_r()
firm.get_w()
household.get_BQ()
tax.replacement_rate_vals()
tax.revenue()
utils.convex_combo()
utils.pct_diff_func()
OBJECTS CREATED WITHIN FUNCTION:
b_guess = [S,] vector, initial guess at household savings
n_guess = [S,] vector, initial guess at household labor supply
b_s = [S,] vector, wealth enter period with
b_splus1 = [S,] vector, household savings
b_splus2 = [S,] vector, household savings one period ahead
BQ = scalar, aggregate bequests to lifetime income group
theta = scalar, replacement rate for social security benenfits
error1 = [S,] vector, errors from FOC for savings
error2 = [S,] vector, errors from FOC for labor supply
tax1 = [S,] vector, total income taxes paid
cons = [S,] vector, household consumption
OBJECTS CREATED WITHIN FUNCTION - SMALL OPEN ONLY
Bss = scalar, aggregate household wealth in the steady state
BIss = scalar, aggregate household net investment in the steady state
RETURNS: solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
OUTPUT: None
--------------------------------------------------------------------
'''
bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params = params
J, S, T, BW, beta, sigma, alpha, gamma, epsilon, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, tau_bq, rho, omega_SS, budget_balance, \
alpha_T, debt_ratio_ss, tau_b, delta_tau,\
lambdas, imm_rates, e, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
chi_b, chi_n = chi_params
maxiter, mindist_SS = iterative_params
small_open, ss_firm_r, ss_hh_r = small_open_params
# Rename the inputs
r = rss
w = wss
T_H = T_Hss
factor = factor_ss
if budget_balance == False:
if baseline_spending == True:
Y = Yss
else:
Y = T_H / alpha_T
if small_open == True:
r = ss_hh_r
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, Y and
# factor
if budget_balance:
outer_loop_vars = (bssmat, nssmat, r, w, T_H, factor)
else:
outer_loop_vars = (bssmat, nssmat, r, w, Y, T_H, factor)
inner_loop_params = (ss_params, income_tax_params, chi_params,
small_open_params)
euler_errors, bssmat, nssmat, new_r, new_w, \
new_T_H, new_Y, new_factor, new_BQ, average_income_model = inner_loop(outer_loop_vars, inner_loop_params, baseline, baseline_spending)
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
if budget_balance:
T_H = utils.convex_combo(new_T_H, T_H, nu)
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_T_H, T_H)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
Y = utils.convex_combo(new_Y, Y, nu)
if Y != 0:
dist = np.array(
[utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_Y, Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
# If Y is zero (if there is no output), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array(
[utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] + [abs(new_Y - Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
print 'New value of nu:', nu
iteration += 1
print "Iteration: %02d" % iteration, " Distance: ", dist
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
bssmat_s = np.append(np.zeros((1, J)), bssmat[:-1, :], axis=0)
bssmat_splus1 = bssmat
rss = r
wss = w
factor_ss = factor
T_Hss = T_H
Lss_params = (e, omega_SS.reshape(S, 1), lambdas, 'SS')
Lss = firm.get_L(nssmat, Lss_params)
if small_open == False:
Kss_params = (omega_SS.reshape(S, 1), lambdas, imm_rates, g_n_ss, 'SS')
Bss = household.get_K(bssmat_splus1, Kss_params)
if budget_balance:
debt_ss = 0.0
else:
debt_ss = debt_ratio_ss * Y
Kss = Bss - debt_ss
Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
Iss = firm.get_I(bssmat_splus1, Kss, Kss, Iss_params)
else:
# Compute capital (K) and wealth (B) separately
Kss_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
Kss = firm.get_K(Lss, ss_firm_r, Kss_params)
Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
InvestmentPlaceholder = np.zeros(bssmat_splus1.shape)
Iss = firm.get_I(InvestmentPlaceholder, Kss, Kss, Iss_params)
Bss_params = (omega_SS.reshape(S, 1), lambdas, imm_rates, g_n_ss, 'SS')
Bss = household.get_K(bssmat_splus1, Bss_params)
BIss_params = (0.0, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
BIss = firm.get_I(bssmat_splus1, Bss, Bss, BIss_params)
if budget_balance:
debt_ss = 0.0
else:
debt_ss = debt_ratio_ss * Y
# Yss_params = (alpha, Z)
Yss_params = (Z, gamma, epsilon)
Yss = firm.get_Y(Kss, Lss, Yss_params)
# Verify that T_Hss = alpha_T*Yss
# transfer_error = T_Hss - alpha_T*Yss
# if np.absolute(transfer_error) > mindist_SS:
# print 'Transfers exceed alpha_T percent of GDP by:', transfer_error
# err = "Transfers do not match correct share of GDP in SS_solver"
# raise RuntimeError(err)
BQss = new_BQ
theta_params = (e, S, retire)
theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, theta_params)
# Next 5 lines pulled out of inner_loop where they are used to calculate tax revenue. Now calculating G to balance gov't budget.
b_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat[:-1, :]))
lump_sum_params = (e, lambdas.reshape(1, J), omega_SS.reshape(S, 1), 'SS',
etr_params, theta, tau_bq, tau_payroll, h_wealth,
p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
revenue_ss = tax.revenue(new_r, new_w, b_s, nssmat, new_BQ, Yss, Lss, Kss,
factor, lump_sum_params)
r_gov_ss = rss
debt_service_ss = r_gov_ss * debt_ratio_ss * Yss
new_borrowing = debt_ratio_ss * Yss * ((1 + g_n_ss) * np.exp(g_y) - 1)
# government spends such that it expands its debt at the same rate as GDP
if budget_balance:
Gss = 0.0
else:
Gss = revenue_ss + new_borrowing - (T_Hss + debt_service_ss)
# solve resource constraint
etr_params_3D = np.tile(
np.reshape(etr_params, (S, 1, etr_params.shape[1])), (1, J, 1))
mtrx_params_3D = np.tile(
np.reshape(mtrx_params, (S, 1, mtrx_params.shape[1])), (1, J, 1))
'''
------------------------------------------------------------------------
The code below is to calulate and save model MTRs
- only exists to help debug
------------------------------------------------------------------------
'''
# etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
# etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
# mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
# mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
# e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
# nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
# mtry_ss_params = (e_extended[1:,:], etr_params_extended_3D, mtry_params_extended_3D, analytical_mtrs)
# mtry_ss = tax.MTR_capital(rss, wss, bssmat_splus1, nss_extended[1:,:], factor_ss, mtry_ss_params)
# mtrx_ss_params = (e, etr_params_3D, mtrx_params_3D, analytical_mtrs)
# mtrx_ss = tax.MTR_labor(rss, wss, bssmat_s, nssmat, factor_ss, mtrx_ss_params)
# np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
# np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
# solve resource constraint
taxss_params = (e, lambdas, 'SS', retire, etr_params_3D, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
taxss = tax.total_taxes(rss, wss, bssmat_s, nssmat, BQss, factor_ss, T_Hss,
None, False, taxss_params)
css_params = (e, lambdas.reshape(1, J), g_y)
cssmat = household.get_cons(rss, wss, bssmat_s, bssmat_splus1, nssmat,
BQss.reshape(1, J), taxss, css_params)
biz_params = (tau_b, delta_tau)
business_revenue = tax.get_biz_tax(wss, Yss, Lss, Kss, biz_params)
Css_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css = household.get_C(cssmat, Css_params)
if small_open == False:
resource_constraint = Yss - (Css + Iss + Gss)
print 'Yss= ', Yss, '\n', 'Gss= ', Gss, '\n', 'Css= ', Css, '\n', 'Kss = ', Kss, '\n', 'Iss = ', Iss, '\n', 'Lss = ', Lss, '\n', 'Debt service = ', debt_service_ss
print 'D/Y:', debt_ss / Yss, 'T/Y:', T_Hss / Yss, 'G/Y:', Gss / Yss, 'Rev/Y:', revenue_ss / Yss, 'business rev/Y: ', business_revenue / Yss, 'Int payments to GDP:', (
rss * debt_ss) / Yss
print 'Check SS budget: ', Gss - (np.exp(g_y) * (1 + g_n_ss) - 1 -
rss) * debt_ss - revenue_ss + T_Hss
print 'resource constraint: ', resource_constraint
else:
# include term for current account
resource_constraint = Yss + new_borrowing - (Css + BIss + Gss) + (
ss_hh_r * Bss - (delta + ss_firm_r) * Kss - debt_service_ss)
print 'Yss= ', Yss, '\n', 'Css= ', Css, '\n', 'Bss = ', Bss, '\n', 'BIss = ', BIss, '\n', 'Kss = ', Kss, '\n', 'Iss = ', Iss, '\n', 'Lss = ', Lss, '\n', 'T_H = ', T_H, '\n', 'Gss= ', Gss
print 'D/Y:', debt_ss / Yss, 'T/Y:', T_Hss / Yss, 'G/Y:', Gss / Yss, 'Rev/Y:', revenue_ss / Yss, 'Int payments to GDP:', (
rss * debt_ss) / Yss
print 'resource constraint: ', resource_constraint
if Gss < 0:
print 'Steady state government spending is negative to satisfy budget'
if ENFORCE_SOLUTION_CHECKS and np.absolute(
resource_constraint) > mindist_SS:
print 'Resource Constraint Difference:', resource_constraint
err = "Steady state aggregate resource constraint not satisfied"
raise RuntimeError(err)
# check constraints
household.constraint_checker_SS(bssmat, nssmat, cssmat, ltilde)
euler_savings = euler_errors[:S, :]
euler_labor_leisure = euler_errors[S:, :]
'''
------------------------------------------------------------------------
Return dictionary of SS results
------------------------------------------------------------------------
'''
output = {
'Kss': Kss,
'bssmat': bssmat,
'Bss': Bss,
'Lss': Lss,
'Css': Css,
'Iss': Iss,
'nssmat': nssmat,
'Yss': Yss,
'wss': wss,
'rss': rss,
'theta': theta,
'BQss': BQss,
'factor_ss': factor_ss,
'bssmat_s': bssmat_s,
'cssmat': cssmat,
'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss,
'Gss': Gss,
'revenue_ss': revenue_ss,
'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure,
'chi_n': chi_n,
'chi_b': chi_b
}
return output
beq_ut = chi_b.reshape(1, J) * (rho.reshape(S, 1)) * \
(savings**(1 - sigma) - 1) / (1 - sigma)
utility = ((cssmat_init ** (1 - sigma) - 1) / (1 - sigma)) + chi_n.reshape(S, 1) * \
(b_ellipse * (1 - (nssmat_init / ltilde)**upsilon) ** (1 / upsilon) + k_ellipse)
utility += beq_ut
utility_init = utility.sum(0)
T_Hss_init = T_Hss
Kss_init = Kss
Lss_init = Lss
c_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css_init = household.get_C(cssmat, c_params)
i_params = (delta, g_y, omega_SS.reshape(1,S), lambdas, imm_rates, g_n_ss, 'SS')
iss_init = firm.get_I(bssmat_splus1, Kss_init, Kss_init, i_params)
income_init = cssmat + iss_init
# print (income_init*omega_SS).sum()
# print Css + delta * Kss
# print Kss
# print Lss
# print Css_init
# print (utility_init * omega_SS).sum()
the_inequalizer(income_init, omega_SS, lambdas, S, J)
'''
------------------------------------------------------------------------
SS baseline graphs
------------------------------------------------------------------------
'''
def run_time_path_iteration(Kss,
Lss,
Yss,
BQss,
theta,
parameters,
g_n_vector,
omega_stationary,
K0,
b_sinit,
b_splus1init,
L0,
Y0,
r0,
BQ0,
T_H_0,
tax0,
c0,
initial_b,
initial_n,
factor_ss,
tau_bq,
chi_b,
chi_n,
get_baseline=False,
output_dir="./OUTPUT",
**kwargs):
TPI_FIG_DIR = output_dir
# Initialize Time paths
domain = np.linspace(0, T, T)
Kinit = (-1 / (domain + 1)) * (Kss - K0) + Kss
Kinit[-1] = Kss
Kinit = np.array(list(Kinit) + list(np.ones(S) * Kss))
Linit = np.ones(T + S) * Lss
Yinit = firm.get_Y(Kinit, Linit, parameters)
winit = firm.get_w(Yinit, Linit, parameters)
rinit = firm.get_r(Yinit, Kinit, parameters)
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQinit = np.array(BQinit)
T_H_init = np.ones(T + S) * T_Hss
# Make array of initial guesses
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
Kpath_TPI = list(Kinit) + list(np.ones(10) * Kss)
Lpath_TPI = list(Linit) + list(np.ones(10) * Lss)
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
plt.figure()
plt.axhline(y=Kss,
color='black',
linewidth=2,
label=r"Steady State $\hat{K}$",
ls='--')
plt.plot(np.arange(T + 10),
Kpath_TPI[:T + 10],
'b',
linewidth=2,
label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(
opt.fsolve(SS_TPI_firstdoughnutring,
[guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1],
initial_b, factor_ss, j, parameters, theta,
tau_bq),
xtol=1e-13))
# if np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1, j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq)).max() > 1e-6:
# print 'minidoughnut:',
# np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1,
# j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b,
# factor_ss, j, parameters, theta, tau_bq)).max()
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j],
S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j,
s, 0, parameters, theta, tau_bq, rho, lambdas, e,
initial_b, chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
# if abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n))).max() > 1e-6:
# print 's-loop:',
# abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit,
# BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters,
# theta, tau_bq, rho, lambdas, e, initial_b, chi_b,
# chi_n))).max()
for t in xrange(0, T):
b_guesses_to_use = .75 * \
np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j,
None, t, parameters, theta, tau_bq, rho, lambdas, e,
None, chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(
Steady_state_TPI_solver(inputs, winit, rinit, BQinit[:, j],
T_H_init, factor_ss, j, None, t,
parameters, theta, tau_bq, rho,
lambdas, e, None, chi_b, chi_n))
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
Kinit = household.get_K(b_mat[:T],
omega_stationary[:T].reshape(T, S, 1),
lambdas.reshape(1, 1,
J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T],
omega_stationary[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
Ynew = firm.get_Y(Kinit, Linit, parameters)
wnew = firm.get_w(Ynew, Linit, parameters)
rnew = firm.get_r(Ynew, Kinit, parameters)
# the following needs a g_n term
BQnew = household.get_BQ(rnew.reshape(T, 1), b_mat[:T],
omega_stationary[:T].reshape(T, S, 1),
lambdas.reshape(1, 1,
J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
T_H_new = np.array(
list(
tax.get_lump_sum(
rnew.reshape(T, 1, 1), bmat_s, wnew.reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T], BQnew.reshape(T, 1, J),
lambdas.reshape(1, 1, J), factor_ss, omega_stationary[:T].
reshape(T, S, 1), 'TPI', parameters, theta, tau_bq)) +
[T_Hss] * S)
winit[:T] = utils.convex_combo(wnew, winit[:T], parameters)
rinit[:T] = utils.convex_combo(rnew, rinit[:T], parameters)
BQinit[:T] = utils.convex_combo(BQnew, BQinit[:T], parameters)
T_H_init[:T] = utils.convex_combo(T_H_new[:T], T_H_init[:T],
parameters)
guesses_b = utils.convex_combo(b_mat, guesses_b, parameters)
guesses_n = utils.convex_combo(n_mat, guesses_n, parameters)
if T_H_init.all() != 0:
TPIdist = np.array(
list(utils.perc_dif_func(rnew, rinit[:T])) +
list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) +
list(utils.perc_dif_func(wnew, winit[:T])) +
list(utils.perc_dif_func(T_H_new, T_H_init))).max()
else:
TPIdist = np.array(
list(utils.perc_dif_func(rnew, rinit[:T])) +
list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) +
list(utils.perc_dif_func(wnew, winit[:T])) +
list(np.abs(T_H_new, T_H_init))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
if TPIiter > 10:
if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
nu /= 2
print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
print 'Computing final solutions'
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(
opt.fsolve(SS_TPI_firstdoughnutring,
[guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1],
initial_b, factor_ss, j, parameters, theta,
tau_bq),
xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0,
parameters, theta, tau_bq, rho, lambdas, e, initial_b,
chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None,
t, parameters, theta, tau_bq, rho, lambdas, e, None,
chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(
Steady_state_TPI_solver(inputs, winit, rinit, BQinit[:, j],
T_H_init, factor_ss, j, None, t,
parameters, theta, tau_bq, rho,
lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
tax_path = tax.total_taxes(rinit[:T].reshape(T, 1, 1),
b_s, winit[:T].reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T],
BQinit[:T, :].reshape(T, 1, J), lambdas,
factor_ss, T_H_init[:T].reshape(T, 1, 1), None,
'TPI', False, parameters, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1),
b_s, winit[:T].reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T],
BQinit[:T].reshape(T, 1, J),
lambdas.reshape(1, 1, J), b_splus1, parameters,
tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[:T].reshape(T, S, 1),
lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1], Kpath_TPI[:T], delta, g_y,
g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print 'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_path[t], t,
parameters)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Kpath_TPI': Kpath_TPI,
'b_mat': b_mat,
'c_path': c_path,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI,
'BQpath_TPI': BQpath_TPI,
'n_mat': n_mat,
'rinit': rinit,
'Yinit': Yinit,
'T_H_init': T_H_init,
'tax_path': tax_path,
'winit': winit
}
if get_baseline:
tpi_init_dir = os.path.join(output_dir, "TPIinit")
utils.mkdirs(tpi_init_dir)
tpi_init_vars = os.path.join(tpi_init_dir, "TPIinit_vars.pkl")
pickle.dump(output, open(tpi_init_vars, "wb"))
else:
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
def run_TPI(income_tax_params, tpi_params, iterative_params, initial_values, SS_values, output_dir="./OUTPUT"):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BQ_dist, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, e, retire, mean_income_data,\
factor, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n = tpi_params
K0, b_sinit, b_splus1init, L0, Y0,\
w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n = initial_values
Kss, Lss, rss, wss, BQss, T_Hss, bssmat_splus1, nssmat = SS_values
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
domain = np.linspace(0, T, T)
K_init = (-1 / (domain + 1)) * (Kss - K0) + Kss
K_init[-1] = Kss
K_init = np.array(list(K_init) + list(np.ones(S) * Kss))
L_init = np.ones(T + S) * Lss
K = K_init
L = L_init
Y_params = (alpha, Z)
Y = firm.get_Y(K, L, Y_params)
w = firm.get_w(Y, L, alpha)
r_params = (alpha, delta)
r = firm.get_r(Y, K, r_params)
BQ = np.zeros((T + S, J))
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
if T_Hss < 1e-13 and T_Hss > 0.0 :
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2
# Make array of initial guesses for labor supply and savings
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
K_plot = list(K) + list(np.ones(10) * Kss)
L_plot = list(L) + list(np.ones(10) * Lss)
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), Kpath_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
# theta_params = (e[-1, j], 1, omega[0].reshape(S, 1), lambdas[j])
# theta = tax.replacement_rate_vals(n, w, factor, theta_params)
theta = np.zeros((J,))
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, theta, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
K[:T] = household.get_K(b_mat[:T], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
wnew = firm.get_w(Ynew[:T], L[:T], alpha)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
BQ_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat[:T,:,:], BQ_params)
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[1:T + 1, :, :]
TH_tax_params = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
TH_tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
T_H_params = (np.tile(e.reshape(1, S, J),(T,1,1)), BQ_dist, lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
TH_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
T_H_new = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, T_H_params)) + [T_Hss] * S)
w[:T] = utils.convex_combo(wnew[:T], w[:T], nu)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
if T_H.all() != 0:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
else:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(np.abs(T_H_new[:T], T_H[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
raise RuntimeError("Transition path equlibrium not found")
Y[:T] = Ynew
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, theta, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
K[:T] = household.get_K(b_mat[:T], K_params) # this is what old code does, but it's strange - why use
# b_mat -- what is going on with initial period, etc.
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), BQ_dist, lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
cons_params = (e.reshape(1, S, J), BQ_dist, lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(omega[:T].reshape(T,S,1), r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
I_params = (delta, g_y, g_n_vector[:T])
I = firm.get_I(K[1:T+1], K[:T], I_params)
print 'Resource Constraint Difference:', Y[:T] - C[:T] - I[:T]
print'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found")
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ,
'T_H': T_H, 'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
'c_path': c_path, 'tax_path': tax_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
'BQ': BQ, 'T_H': T_H, 'r': r, 'w': w,
'tax_path': tax_path}
# Non-stationary output
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
return output, macro_output
def run_TPI(income_tax_params, tpi_params, iterative_params, small_open_params, initial_values, SS_values, fiscal_params, biz_tax_params, output_dir="./OUTPUT", baseline_spending=False):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BW, beta, sigma, alpha, gamma, epsilon, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, imm_rates, e, retire, mean_income_data,\
factor, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n, theta = tpi_params
# K0, b_sinit, b_splus1init, L0, Y0,\
# w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n, omega_S_preTP = initial_values
small_open, tpi_firm_r, tpi_hh_r = small_open_params
B0, b_sinit, b_splus1init, factor, initial_b, initial_n, omega_S_preTP, initial_debt = initial_values
Kss, Bss, Lss, rss, wss, BQss, T_Hss, revenue_ss, bssmat_splus1, nssmat, Yss, Gss = SS_values
tau_b, delta_tau = biz_tax_params
if baseline_spending==False:
budget_balance, ALPHA_T, ALPHA_G, tG1, tG2, rho_G, debt_ratio_ss = fiscal_params
else:
budget_balance, ALPHA_T, ALPHA_G, tG1, tG2, rho_G, debt_ratio_ss, T_Hbaseline, Gbaseline = fiscal_params
print 'Government spending breakpoints are tG1: ', tG1, '; and tG2:', tG2
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, T, T)
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b#np.zeros((T + S, S, J))
n_mat = guesses_n#np.zeros((T + S, S, J))
ind = np.arange(S)
L_init = np.ones((T+S,))*Lss
B_init = np.ones((T+S,))*Bss
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L_init[:T] = firm.get_L(n_mat[:T], L_params)
B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
B_init[1:T] = household.get_K(b_mat[:T-1], B_params)
B_init[0] = B0
if small_open == False:
if budget_balance:
K_init = B_init
else:
K_init = B_init * Kss/Bss
else:
K_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K_init = firm.get_K(L_init, tpi_firm_r, K_params)
K = K_init
# if np.any(K < 0):
# print 'K_init has negative elements. Setting them positive to prevent NAN.'
# K[:T] = np.fmax(K[:T], 0.05*B[:T])
L = L_init
B = B_init
Y_params = (Z, gamma, epsilon)
Y = firm.get_Y(K, L, Y_params)
w_params = (Z, gamma, epsilon)
w = firm.get_w(Y, L, w_params)
if small_open == False:
r_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
r = firm.get_r(Y, K, r_params)
else:
r = tpi_hh_r
BQ = np.zeros((T + S, J))
BQ0_params = (omega_S_preTP.reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r[0], initial_b, BQ0_params)
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
if budget_balance:
if np.abs(T_Hss) < 1e-13 :
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2
REVENUE = T_H
G = np.zeros(T + S)
elif baseline_spending==False:
T_H = ALPHA_T * Y
elif baseline_spending==True:
T_H = T_Hbaseline
T_H_new = T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some inputs
# D = np.zeros(T + S)
D = debt_ratio_ss*Y
omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
tax_params = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
# print 'D/Y:', D[:T]/Y[:T]
# print 'T/Y:', T_H[:T]/Y[:T]
# print 'G/Y:', G[:T]/Y[:T]
# print 'Int payments to GDP:', (r[:T]*D[:T])/Y[:T]
# quit()
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
report_tG1 = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
print 'analytical mtrs in tpi = ', analytical_mtrs
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
#K_plot = list(K) + list(np.ones(10) * Kss)
D_plot = list(D) + list(np.ones(10) * Yss * debt_ratio_ss)
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), D_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_D"))
if report_tG1 is True:
print '\tAt time tG1-1:'
print '\t\tG = ', G[tG1-1]
print '\t\tK = ', K[tG1-1]
print '\t\tr = ', r[tG1-1]
print '\t\tD = ', D[tG1-1]
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
#L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI') # defined above
L[:T] = firm.get_L(n_mat[:T], L_params)
#B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI') # defined above
B[1:T] = household.get_K(bmat_splus1[:T-1], B_params)
if np.any(B) < 0:
print 'B has negative elements. B[0:9]:', B[0:9]
print 'B[T-2:T]:', B[T-2,T]
if small_open == False:
if budget_balance:
K[:T] = B[:T]
else:
if baseline_spending == False:
Y = T_H/ALPHA_T #SBF 3/3: This seems totally unnecessary as both these variables are defined above.
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau) # define above
REVENUE = np.array(list(tax.revenue(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQ[:T].reshape(T, 1, J), Y[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
Dnew, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
K[:T] = B[:T] - Dnew[:T]
if np.any(K < 0):
print 'K has negative elements. Setting them positive to prevent NAN.'
K[:T] = np.fmax(K[:T], 0.05*B[:T])
else:
# K_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K[:T] = firm.get_K(L[:T], tpi_firm_r[:T], K_params)
Y_params = (Z, gamma, epsilon)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
Y = Ynew
w_params = (Z, gamma, epsilon)
wnew = firm.get_w(Ynew[:T], L[:T], w_params)
if small_open == False:
r_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
else:
rnew = r.copy()
print 'Y and T_H: ', Y[3], T_H[3]
# omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0) # defined above
# BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
# g_n_vector[:T].reshape(T, 1), 'TPI') # defined above
b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau) # defined above
REVENUE = np.array(list(tax.revenue(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), Y[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
if budget_balance:
T_H_new = REVENUE
elif baseline_spending==False:
T_H_new = ALPHA_T[:T] * Y[:T]
# If baseline_spending==True, no need to update T_H, which remains fixed.
if small_open==True and budget_balance==False:
# Loop through years to calculate debt and gov't spending. This is done earlier when small_open=False.
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
Dnew, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
w[:T] = utils.convex_combo(wnew[:T], w[:T], nu)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
# D[:T] = utils.convex_combo(Dnew[:T], D[:T], nu)
D = Dnew
Y[:T] = utils.convex_combo(Ynew[:T], Y[:T], nu)
if baseline_spending==False:
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
print 'r diff: ', (rnew[:T]-r[:T]).max(), (rnew[:T]-r[:T]).min()
print 'w diff: ', (wnew[:T]-w[:T]).max(), (wnew[:T]-w[:T]).min()
print 'BQ diff: ', (BQnew[:T]-BQ[:T]).max(), (BQnew[:T]-BQ[:T]).min()
print 'T_H diff: ', (T_H_new[:T]-T_H[:T]).max(), (T_H_new[:T]-T_H[:T]).min()
if baseline_spending==False:
if T_H.all() != 0:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
else:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(np.abs(T_H[:T]))).max()
else:
# TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
# utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(Dnew[:T], D[:T]))).max()
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(Ynew[:T], Y[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print 'Iteration:', TPIiter
print '\tDistance:', TPIdist
# print 'D/Y:', (D[:T]/Ynew[:T]).max(), (D[:T]/Ynew[:T]).min(), np.median(D[:T]/Ynew[:T])
# print 'T/Y:', (T_H_new[:T]/Ynew[:T]).max(), (T_H_new[:T]/Ynew[:T]).min(), np.median(T_H_new[:T]/Ynew[:T])
# print 'G/Y:', (G[:T]/Ynew[:T]).max(), (G[:T]/Ynew[:T]).min(), np.median(G[:T]/Ynew[:T])
# print 'Int payments to GDP:', ((r[:T]*D[:T])/Ynew[:T]).max(), ((r[:T]*D[:T])/Ynew[:T]).min(), np.median((r[:T]*D[:T])/Ynew[:T])
#
# print 'D/Y:', (D[:T]/Ynew[:T])
# print 'T/Y:', (T_H_new[:T]/Ynew[:T])
# print 'G/Y:', (G[:T]/Ynew[:T])
#
# print 'deficit: ', REVENUE[:T] - T_H_new[:T] - G[:T]
# Loop through years to calculate debt and gov't spending. The re-assignment of G0 & D0 is necessary because Y0 may change in the TPI loop.
if budget_balance == False:
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
D, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
#L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI') # defined above
L[:T] = firm.get_L(n_mat[:T], L_params)
#B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI') # defined above
B[1:T] = household.get_K(bmat_splus1[:T-1], B_params)
if small_open == False:
K[:T] = B[:T] - D[:T]
else:
# K_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K[:T] = firm.get_K(L[:T], tpi_firm_r[:T], K_params)
# Y_params previously set to = (Z, gamma, epsilon)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
# testing for change in Y
ydiff = Ynew[:T] - Y[:T]
ydiff_max = np.amax(np.abs(ydiff))
print 'ydiff_max = ', ydiff_max
w_params = (Z, gamma, epsilon)
wnew = firm.get_w(Ynew[:T], L[:T], w_params)
if small_open == False:
# r_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
else:
rnew = r
# Note: previously, Y was not reassigned to equal Ynew at this point.
Y = Ynew[:]
# omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
# BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
# g_n_vector[:T].reshape(T, 1), 'TPI')
b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
REVENUE = np.array(list(tax.revenue(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), Ynew[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
if budget_balance==False:
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
D, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
if small_open == False:
I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
I = firm.get_I(bmat_splus1[:T], K[1:T+1], K[:T], I_params)
rc_error = Y[:T] - C[:T] - I[:T] - G[:T]
else:
#InvestmentPlaceholder = np.zeros(bmat_splus1[:T].shape)
#I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
I = (1+g_n_vector[:T])*np.exp(g_y)*K[1:T+1] - (1.0 - delta) * K[:T] #firm.get_I(InvestmentPlaceholder, K[1:T+1], K[:T], I_params)
BI_params = (0.0, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
BI = firm.get_I(bmat_splus1[:T], B[1:T+1], B[:T], BI_params)
new_borrowing = D[1:T]*(1+g_n_vector[1:T])*np.exp(g_y) - D[:T-1]
rc_error = Y[:T-1] + new_borrowing - (C[:T-1] + BI[:T-1] + G[:T-1] ) + (tpi_hh_r[:T-1] * B[:T-1] - (delta + tpi_firm_r[:T-1])*K[:T-1] - tpi_hh_r[:T-1]*D[:T-1])
#print 'Y(T-1):', Y[T-1], '\n','C(T-1):', C[T-1], '\n','K(T-1):', K[T-1], '\n','B(T-1):', B[T-1], '\n','BI(T-1):', BI[T-1], '\n','I(T-1):', I[T-1]
rce_max = np.amax(np.abs(rc_error))
print 'Max absolute value resource constraint error:', rce_max
print'Checking time path for violations of constraints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
# print 'Max Euler error, savings: ', eul_savings
# print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ,
'REVENUE': REVENUE, 'T_H': T_H, 'G': G, 'D': D,
'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
'c_path': c_path, 'tax_path': tax_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
'BQ': BQ, 'T_H': T_H, 'r': r, 'w': w,
'tax_path': tax_path}
growth = (1+g_n_vector)*np.exp(g_y)
with open('TPI_output.csv', 'wb') as csvfile:
tpiwriter = csv.writer(csvfile)
tpiwriter.writerow(Y)
tpiwriter.writerow(D)
tpiwriter.writerow(REVENUE)
tpiwriter.writerow(G)
tpiwriter.writerow(T_H)
tpiwriter.writerow(C)
tpiwriter.writerow(K)
tpiwriter.writerow(I)
tpiwriter.writerow(r)
if small_open == True:
tpiwriter.writerow(B)
tpiwriter.writerow(BI)
tpiwriter.writerow(new_borrowing)
tpiwriter.writerow(growth)
tpiwriter.writerow(rc_error)
tpiwriter.writerow(ydiff)
if np.any(G) < 0:
print 'Government spending is negative along transition path to satisfy budget'
if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
raise RuntimeError("Transition path equlibrium not found (TPIdist)")
if ((np.any(np.absolute(rc_error) >= mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found (rc_error)")
if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found (eulers)")
# Non-stationary output
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
return output, macro_output
def run_TPI(income_tax_params,
tpi_params,
iterative_params,
initial_values,
SS_values,
fix_transfers=False,
output_dir="./OUTPUT"):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, imm_rates, e, retire, mean_income_data,\
factor, T_H_baseline, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n, theta = tpi_params
K0, b_sinit, b_splus1init, factor, initial_b, initial_n, omega_S_preTP = initial_values
Kss, Lss, rss, wss, BQss, T_Hss, Gss, bssmat_splus1, nssmat = SS_values
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
domain = np.linspace(0, T, T)
r = np.ones(T + S) * rss
BQ = np.zeros((T + S, J))
BQ0_params = (omega_S_preTP.reshape(S, 1), lambdas, rho.reshape(S, 1),
g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r[0], initial_b, BQ0_params)
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
# print "BQ values = ", BQ[0, :], BQ[100, :], BQ[-1, :], BQss
# print "K0 vs Kss = ", K0-Kss
if fix_transfers:
T_H = T_H_baseline
else:
if np.abs(T_Hss) < 1e-13:
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2 * (r / rss)
G = np.ones(T + S) * Gss
# # print "T_H values = ", T_H[0], T_H[100], T_H[-1], T_Hss
# # print "omega diffs = ", (omega_S_preTP-omega[-1]).max(), (omega[10]-omega[-1]).max()
#
# Make array of initial guesses for labor supply and savings
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
# print 'diff btwn start and end b: ', (guesses_b[0]-guesses_b[-1]).max()
#
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
# b_mat = np.zeros((T + S, S, J))
# n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
# # print 'diff btwn start and end n: ', (guesses_n[0]-guesses_n[-1]).max()
#
# # find economic aggregates
K = np.zeros(T + S)
L = np.zeros(T + S)
K[0] = K0
K_params = (omega[:T - 1].reshape(T - 1, S, 1), lambdas.reshape(1, 1, J),
imm_rates[:T - 1].reshape(T - 1, S, 1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(guesses_b[:T - 1], K_params)
K[T:] = Kss
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(guesses_n[:T], L_params)
L[T:] = Lss
Y_params = (alpha, Z)
Y = firm.get_Y(K, L, Y_params)
r_params = (alpha, delta)
r[:T] = firm.get_r(Y[:T], K[:T], r_params)
# uncomment lines below if want to use starting values from prior run
r = TPI_START_VALUES['r']
K = TPI_START_VALUES['K']
L = TPI_START_VALUES['L']
Y = TPI_START_VALUES['Y']
T_H = TPI_START_VALUES['T_H']
BQ = TPI_START_VALUES['BQ']
G = TPI_START_VALUES['G']
guesses_b = TPI_START_VALUES['b_mat']
guesses_n = TPI_START_VALUES['n_mat']
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
# print 'analytical mtrs in tpi = ', analytical_mtrs
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
K_plot = list(K) + list(np.ones(10) * Kss)
L_plot = list(L) + list(np.ones(10) * Lss)
plt.figure()
plt.axhline(y=Kss,
color='black',
linewidth=2,
label=r"Steady State $\hat{K}$",
ls='--')
plt.plot(np.arange(T + 10),
Kpath_plot[:T + 10],
'b',
linewidth=2,
label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
guesses = (guesses_b, guesses_n)
w_params = (Z, alpha, delta)
w = firm.get_w_from_r(r, w_params)
# print 'r and rss diff = ', r-rss
# print 'w and wss diff = ', w-wss
# print 'BQ and BQss diff = ', BQ-BQss
# print 'T_H and T_Hss diff = ', T_H - T_Hss
# print 'guess b and bss = ', (bssmat_splus1 - guesses_b).max()
# print 'guess n and nss = ', (nssmat - guesses_n).max()
outer_loop_vars = (r, w, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values,
ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars,
inner_loop_params)
# print 'guess b and bss = ', (b_mat - guesses_b).max()
# print 'guess n and nss over time = ', (n_mat - guesses_n).max(axis=2).max(axis=1)
# print 'guess n and nss over age = ', (n_mat - guesses_n).max(axis=0).max(axis=1)
# print 'guess n and nss over ability = ', (n_mat - guesses_n).max(axis=0).max(axis=0)
# quit()
print 'Max Euler error: ', (np.abs(euler_errors)).max()
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T - 1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
K[0] = K0
K_params = (omega[:T - 1].reshape(T - 1, S,
1), lambdas.reshape(1, 1, J),
imm_rates[:T - 1].reshape(T - 1, S,
1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(bmat_splus1[:T - 1], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
# print 'K diffs = ', K-K0
# print 'L diffs = ', L-L[0]
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
wnew = firm.get_w_from_r(rnew, w_params)
omega_shift = np.append(omega_S_preTP.reshape(1, S),
omega[:T - 1, :],
axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J),
rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
# b_mat_shift = np.append(np.reshape(initial_b, (1, S, J)),
# b_mat[:T-1, :, :], axis=0)
b_mat_shift = bmat_splus1[:T, :, :]
# print 'b diffs = ', (bmat_splus1[100, :, :] - initial_b).max(), (bmat_splus1[0, :, :] - initial_b).max(), (bmat_splus1[1, :, :] - initial_b).max()
# print 'r diffs = ', rnew[1]-r[1], rnew[100]-r[100], rnew[-1]-r[-1]
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift,
BQ_params)
BQss2 = np.empty(J)
for j in range(J):
BQss_params = (omega[1, :], lambdas[j], rho, g_n_vector[1], 'SS')
BQss2[j] = household.get_BQ(rnew[1], bmat_splus1[1, :, j],
BQss_params)
# print 'BQ test = ', BQss2-BQss, BQss-BQnew[1], BQss-BQnew[100], BQss-BQnew[-1]
total_tax_params = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
total_tax_params[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
tax_receipt_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)),
lambdas.reshape(1, 1,
J), omega[:T].reshape(T, S,
1), 'TPI',
total_tax_params, theta, tau_bq, tau_payroll,
h_wealth, p_wealth, m_wealth, retire, T, S, J)
net_tax_receipts = np.array(
list(
tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1), (
1, S, J)), np.tile(wnew[:T].reshape(T, 1, 1), (
1, S, J)), bmat_s, n_mat[:T, :, :], BQnew[:T].reshape(
T, 1, J), factor, tax_receipt_params)) +
[T_Hss] * S)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
if fix_transfers:
T_H_new = T_H
G[:T] = net_tax_receipts[:T] - T_H[:T]
else:
T_H_new = net_tax_receipts
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
G[:T] = 0.0
etr_params_path = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
tax_path_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), lambdas,
'TPI', retire, etr_params_path, h_wealth, p_wealth,
m_wealth, tau_payroll, theta, tau_bq, J, S)
b_to_use = np.zeros((T, S, J))
b_to_use[0, 1:, :] = initial_b[:-1, :]
b_to_use[1:, 1:, :] = b_mat[:T - 1, :-1, :]
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1), (1, S, J)),
np.tile(w[:T].reshape(T, 1, 1),
(1, S, J)), b_to_use,
n_mat[:T, :, :], BQ[:T, :].reshape(T, 1, J),
factor, T_H[:T].reshape(T, 1, 1), None,
False, tax_path_params)
y_path = (np.tile(r[:T].reshape(T, 1, 1),
(1, S, J)) * b_to_use[:T, :, :] +
np.tile(w[:T].reshape(T, 1, 1),
(1, S, J)) * np.tile(e.reshape(1, S, J),
(T, 1, 1)) * n_mat[:T, :, :])
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1,
1), w[:T].reshape(T, 1, 1),
b_to_use[:T, :, :], b_mat[:T, :, :],
n_mat[:T, :, :], BQ[:T].reshape(T, 1, J),
tax_path, cons_params)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
if T_H.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
print 'r dist = ', np.array(
list(utils.pct_diff_func(rnew[:T], r[:T]))).max()
print 'BQ dist = ', np.array(
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten())).max()
print 'T_H dist = ', np.array(
list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
print 'T_H path = ', T_H[:20]
# print 'r old = ', r[:T]
# print 'r new = ', rnew[:T]
# print 'K old = ', K[:T]
# print 'L old = ', L[:T]
# print 'income = ', y_path[:, :, -1]
# print 'taxes = ', tax_path[:, :, -1]
# print 'labor supply = ', n_mat[:, :, -1]
# print 'max and min labor = ', n_mat.max(), n_mat.min()
# print 'max and min labor = ', np.argmax(n_mat), np.argmin(n_mat)
# print 'max and min labor, j = 7 = ', n_mat[:,:,-1].max(), n_mat[:,:,-1].min()
# print 'max and min labor, j = 6 = ', n_mat[:,:,-2].max(), n_mat[:,:,-2].min()
# print 'max and min labor, j = 5 = ', n_mat[:,:,4].max(), n_mat[:,:,4].min()
# print 'max and min labor, j = 4 = ', n_mat[:,:,3].max(), n_mat[:,:,3].min()
# print 'max and min labor, j = 3 = ', n_mat[:,:,2].max(), n_mat[:,:,2].min()
# print 'max and min labor, j = 2 = ', n_mat[:,:,1].max(), n_mat[:,:,1].min()
# print 'max and min labor, j = 1 = ', n_mat[:,:,0].max(), n_mat[:,:,0].min()
# print 'max and min labor, S = 80 = ', n_mat[:,-1,-1].max(), n_mat[:,-1,-1].min()
# print "number > 1 = ", (n_mat > 1).sum()
# print "number < 0, = ", (n_mat < 0).sum()
# print "number > 1, j=7 = ", (n_mat[:T,:,-1] > 1).sum()
# print "number < 0, j=7 = ", (n_mat[:T,:,-1] < 0).sum()
# print "number > 1, s=80, j=7 = ", (n_mat[:T,-1,-1] > 1).sum()
# print "number < 0, s=80, j=7 = ", (n_mat[:T,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 80= ", (n_mat[:T,-1,-1] > 1).sum()
# print "number < 0, j = 7, age 80= ", (n_mat[:T,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] > 1).sum()
# print "number < 0, j = 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80,-1] > 1).sum()
# print "number < 0, j = 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80 ,-1] < 0).sum()
# diag_dict = {'n_mat': n_mat, 'b_mat': b_mat, 'y_path': y_path, 'c_path': c_path}
# pickle.dump(diag_dict, open('tpi_iter1.pkl', 'wb'))
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(np.abs(T_H_new[:T] - T_H[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
Y[:T] = Ynew
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars,
inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T - 1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
K[0] = K0
K_params = (omega[:T - 1].reshape(T - 1, S, 1), lambdas.reshape(1, 1, J),
imm_rates[:T - 1].reshape(T - 1, S, 1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(bmat_splus1[:T - 1], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
wnew = firm.get_w_from_r(rnew, w_params)
omega_shift = np.append(omega_S_preTP.reshape(1, S),
omega[:T - 1, :],
axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J),
rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, S, J)),
b_mat[:T - 1, :, :],
axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
total_tax_params = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
total_tax_params[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
tax_receipt_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)),
lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1),
'TPI', total_tax_params, theta, tau_bq, tau_payroll,
h_wealth, p_wealth, m_wealth, retire, T, S, J)
net_tax_receipts = np.array(
list(
tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1), (
1, S, J)), np.tile(wnew[:T].reshape(T, 1, 1), (
1, S, J)), bmat_s, n_mat[:T, :, :], BQnew[:T].reshape(
T, 1, J), factor, tax_receipt_params)) + [T_Hss] * S)
if fix_transfers:
G[:T] = net_tax_receipts[:T] - T_H[:T]
else:
T_H[:T] = net_tax_receipts[:T]
G[:T] = 0.0
etr_params_path = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:, :, :, i] = np.tile(
np.reshape(np.transpose(etr_params[:, :T, i]), (T, S, 1)),
(1, 1, J))
tax_path_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), lambdas, 'TPI',
retire, etr_params_path, h_wealth, p_wealth, m_wealth,
tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1), (1, S, J)),
np.tile(w[:T].reshape(T, 1, 1),
(1, S, J)), bmat_s, n_mat[:T, :, :],
BQ[:T, :].reshape(T, 1, J), factor,
T_H[:T].reshape(T, 1, 1), None, False,
tax_path_params)
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1),
bmat_s, bmat_splus1, n_mat[:T, :, :],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas,
imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T + 1], 'TPI')
I = firm.get_I(bmat_splus1[:T], K[1:T + 1], K[:T], I_params)
rc_error = Y[:T] - C[:T] - I[:T] - G[:T]
print 'Resource Constraint Difference:', rc_error
# compute utility
u_params = (sigma, np.tile(chi_n.reshape(1, S, 1),
(T, 1, J)), b_ellipse, ltilde, upsilon,
np.tile(rho.reshape(1, S, 1),
(T, 1, J)), np.tile(chi_b.reshape(1, 1, J), (T, S, 1)))
utility_path = household.get_u(c_path[:T, :, :], n_mat[:T, :, :],
bmat_splus1[:T, :, :], u_params)
# compute before and after-tax income
y_path = (np.tile(r[:T].reshape(T, 1, 1), (1, S, J)) * bmat_s[:T, :, :] +
np.tile(w[:T].reshape(T, 1, 1),
(1, S, J)) * np.tile(e.reshape(1, S, J),
(T, 1, 1)) * n_mat[:T, :, :])
inctax_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), etr_params_path)
y_aftertax_path = (y_path - tax.tau_income(
np.tile(r[:T].reshape(T, 1, 1),
(1, S, J)), np.tile(w[:T].reshape(T, 1, 1), (1, S, J)),
bmat_s[:T, :, :], n_mat[:T, :, :], factor, inctax_params))
# compute after-tax wealth
wtax_params = (h_wealth, p_wealth, m_wealth)
b_aftertax_path = bmat_s[:T, :, :] - tax.tau_wealth(
bmat_s[:T, :, :], wtax_params)
print 'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_path[t], t,
ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y,
'K': K,
'L': L,
'C': C,
'I': I,
'BQ': BQ,
'G': G,
'T_H': T_H,
'r': r,
'w': w,
'b_mat': b_mat,
'n_mat': n_mat,
'c_path': c_path,
'tax_path': tax_path,
'bmat_s': bmat_s,
'utility_path': utility_path,
'b_aftertax_path': b_aftertax_path,
'y_aftertax_path': y_aftertax_path,
'y_path': y_path,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure
}
macro_output = {
'Y': Y,
'K': K,
'L': L,
'C': C,
'I': I,
'BQ': BQ,
'G': G,
'T_H': T_H,
'r': r,
'w': w,
'tax_path': tax_path
}
# if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
# raise RuntimeError("Transition path equlibrium not found")
#
# if ((np.any(np.absolute(rc_error) >= 1e-6))
# and ENFORCE_SOLUTION_CHECKS):
# raise RuntimeError("Transition path equlibrium not found")
#
# if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
# (np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
# and ENFORCE_SOLUTION_CHECKS):
# raise RuntimeError("Transition path equlibrium not found")
return output, macro_output
def run_steady_state(income_tax_parameters,
ss_parameters,
iterative_params,
get_baseline=False,
calibrate_model=False,
output_dir="./OUTPUT"):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S + J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * .05
n_guess = np.ones((S, J)).flatten() * .4 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0.12
factorguess = 70000.0
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:],
chi_params[:J], income_tax_parameters, ss_parameters,
iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve,
guesses,
args=args_,
xtol=mindist_SS,
full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess,
rguess, T_Hguess, factorguess, chi_params[J:],
chi_params[:J], income_tax_parameters, ss_parameters,
iterative_params, tau_bq, rho, lambdas, omega_SS, e,
fsolve_flag)
if calibrate_model:
global Nfeval, value_all, chi_params_all
Nfeval = 1
value_all = np.zeros((10000))
chi_params_all = np.zeros((S + J, 10000))
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_path = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_path, "wb"))
function_to_minimize_X = lambda x: function_to_minimize(
x, chi_params, income_tax_parameters, ss_parameters,
iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S + J)
#chi_params_scalars = opt.minimize(function_to_minimize_X, chi_params_scalars,
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds, callback=callbackF(chi_params_scalars), options=MINIMIZER_OPTIONS).x
# chi_params_scalars = opt.minimize(function_to_minimize, chi_params_scalars,
# args=(chi_params, income_tax_parameters, ss_parameters, iterative_params,
# omega_SS, rho, lambdas, tau_bq, e, output_dir),
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds,
# callback=callbackF(chi_params_scalars,chi_params, income_tax_parameters,
# ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir),
# options=MINIMIZER_OPTIONS).x
chi_params_scalars = opt.minimize(
function_to_minimize,
chi_params_scalars,
args=(chi_params, income_tax_parameters, ss_parameters,
iterative_params, omega_SS, rho, lambdas, tau_bq, e,
output_dir),
method='TNC',
tol=MINIMIZER_TOL,
bounds=bnds,
options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:],
chi_params[:J], income_tax_parameters, ss_parameters,
iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve,
guesses,
args=args_,
xtol=mindist_SS,
full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S,
J), n_guess.reshape(S,
J), wguess,
rguess, T_Hguess, factorguess, chi_params[J:],
chi_params[:J], income_tax_parameters,
ss_parameters, iterative_params, tau_bq, rho,
lambdas, omega_SS, e, fsolve_flag)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_dir = os.path.join(output_dir,
"Saved_moments/SS_baseline_solutions.pkl")
pickle.dump(outputs, open(ss_init_dir, "wb"))
else:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_exp_dir = os.path.join(output_dir,
"Saved_moments/SS_reform_solutions.pkl")
pickle.dump(outputs, open(ss_exp_dir, "wb"))
bssmat = solutions[0:(S - 1) * J].reshape(S - 1, J)
bq = solutions[
(S - 1) * J:S *
J] # technically, this is just the intentional bequests - wealth of those with max age
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2 * S * J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2 * S * J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(S, 1), lambdas,
g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = np.zeros(J) #tax.replacement_rate_vals(
#nssmat, wss, factor_ss, e, J, omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(S, 1),
lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
etr_params_3D = np.tile(
np.reshape(etr_params, (S, 1, etr_params.shape[1])), (1, J, 1))
mtrx_params_3D = np.tile(
np.reshape(mtrx_params, (S, 1, mtrx_params.shape[1])), (1, J, 1))
etr_params_extended = np.append(etr_params,
np.reshape(etr_params[-1, :],
(1, etr_params.shape[1])),
axis=0)[1:, :]
etr_params_extended_3D = np.tile(
np.reshape(etr_params_extended, (S, 1, etr_params_extended.shape[1])),
(1, J, 1))
mtry_params_extended = np.append(mtry_params,
np.reshape(mtry_params[-1, :],
(1, mtry_params.shape[1])),
axis=0)[1:, :]
mtry_params_extended_3D = np.tile(
np.reshape(mtry_params_extended,
(S, 1, mtry_params_extended.shape[1])), (1, J, 1))
e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
mtry_ss = tax.MTR_capital(rss, bssmat_splus1, wss, e_extended[1:, :],
nss_extended[1:, :], factor_ss, analytical_mtrs,
etr_params_extended_3D, mtry_params_extended_3D)
mtrx_ss = tax.MTR_labor(rss, bssmat_s, wss, e, nssmat, factor_ss,
analytical_mtrs, etr_params_3D, mtrx_params_3D)
np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
taxss_params = (J, S, retire,
np.tile(
np.reshape(etr_params, (S, 1, etr_params.shape[1])),
(1, J, 1)), h_wealth, p_wealth, m_wealth, tau_payroll)
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas, factor_ss,
T_Hss, None, 'SS', False, taxss_params, theta,
tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(1, J),
lambdas.reshape(1, J), bssmat_splus1,
ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
constraint_params = ltilde
household.constraint_checker_SS(bssmat, nssmat, cssmat, constraint_params)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(
list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(
wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
b_splus2[:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j],
income_tax_parameters, ss_parameters, theta[j], tau_bq[j], rho,
lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(
wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
BQss[j], factor_ss, T_Hss, chi_n, income_tax_parameters,
ss_parameters, theta[j], tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {
'Kss': Kss,
'bssmat': bssmat,
'Lss': Lss,
'Css': Css,
'nssmat': nssmat,
'Yss': Yss,
'wss': wss,
'rss': rss,
'theta': theta,
'BQss': BQss,
'factor_ss': factor_ss,
'bssmat_s': bssmat_s,
'cssmat': cssmat,
'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss,
'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure,
'chi_n': chi_n,
'chi_b': chi_b
}
utils.mkdirs(os.path.join(output_dir, "SSinit"))
ss_init_dir = os.path.join(output_dir, "SSinit/ss_init_vars.pkl")
pickle.dump(output, open(ss_init_dir, "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init': bssmat_init, 'nssmat_init': nssmat_init}
ss_init_tpi = os.path.join(output_dir, "SSinit/ss_init_tpi_vars.pkl")
pickle.dump(output2, open(ss_init_tpi, "wb"))
return output
def run_steady_state(ss_parameters, iterative_params, get_baseline=False, calibrate_model=False):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
if get_baseline:
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S+J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * .01
n_guess = np.ones((S, J)).flatten() * .5 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0
factorguess = 100000
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
if calibrate_model:
outputs = {'solutions':solutions, 'chi_params':chi_params}
pickle.dump(outputs, open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "wb"))
function_to_minimize_X = lambda x: function_to_minimize(x, chi_params, ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S+J)
chi_params_scalars = opt.minimize(function_to_minimize_X, chi_params_scalars, method='TNC', tol=MINIMIZER_TOL, bounds=bnds, options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S*J]
n_guess = solutions[S*J:2*S*J]
wguess, rguess, factorguess, T_Hguess = solutions[2*S*J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
else:
variables = pickle.load(open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "rb"))
solutions = solutions_dict['solutions']
chi_params = solutions_dict['chi_params']
b_guess = solutions[:S*J]
n_guess = solutions[S*J:2*S*J]
wguess, rguess, factorguess, T_Hguess = solutions[2*S*J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions':solutions, 'chi_params':chi_params}
pickle.dump(outputs, open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "wb"))
else:
outputs = {'solutions':solutions, 'chi_params':chi_params}
pickle.dump(outputs, open("OUTPUT/Saved_moments/SS_experiment_solutions.pkl", "wb"))
bssmat = solutions[0:(S-1) * J].reshape(S-1, J)
bq = solutions[(S-1)*J:S*J]
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2*S*J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2*S*J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(S, 1), lambdas, g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, e, J, omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas, factor_ss, T_Hss, None, 'SS', False, ss_parameters, theta, tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(1, J), lambdas.reshape(1, J), bssmat_splus1, ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
household.constraint_checker_SS(bssmat, nssmat, cssmat, ss_parameters)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j], b_splus2[:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j], ss_parameters, theta[j], tau_bq[j], rho, lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j], BQss[j], factor_ss, T_Hss, chi_n, ss_parameters, theta[j], tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {'Kss':Kss, 'bssmat':bssmat, 'Lss':Lss, 'nssmat':nssmat, 'Yss':Yss,
'wss':wss, 'rss':rss, 'theta':theta, 'BQss':BQss, 'factor_ss':factor_ss,
'bssmat_s':bssmat_s, 'cssmat':cssmat, 'bssmat_splus1':bssmat_splus1,
'T_Hss':T_Hss, 'euler_savings':euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n':chi_n,
'chi_b':chi_b}
if get_baseline:
pickle.dump(output, open("OUTPUT/SSinit/ss_init_vars.pkl", "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init':bssmat_init, 'nssmat_init':nssmat_init}
pickle.dump(output2, open("OUTPUT/SSinit/ss_init_tpi_vars.pkl", "wb"))
else:
pickle.dump(output, open("OUTPUT/SS/ss_vars.pkl", "wb"))
return output
def run_TPI(income_tax_params, tpi_params, iterative_params,
initial_values, SS_values, fix_transfers=False,
output_dir="./OUTPUT"):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, imm_rates, e, retire, mean_income_data,\
factor, T_H_baseline, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n, theta = tpi_params
K0, b_sinit, b_splus1init, factor, initial_b, initial_n, omega_S_preTP = initial_values
Kss, Lss, rss, wss, BQss, T_Hss, Gss, bssmat_splus1, nssmat = SS_values
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
domain = np.linspace(0, T, T)
r = np.ones(T + S) * rss
BQ = np.zeros((T + S, J))
BQ0_params = (omega_S_preTP.reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r[0], initial_b, BQ0_params)
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
# print "BQ values = ", BQ[0, :], BQ[100, :], BQ[-1, :], BQss
# print "K0 vs Kss = ", K0-Kss
if fix_transfers:
T_H = T_H_baseline
else:
if np.abs(T_Hss) < 1e-13 :
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2 * (r/rss)
G = np.ones(T + S) * Gss
# # print "T_H values = ", T_H[0], T_H[100], T_H[-1], T_Hss
# # print "omega diffs = ", (omega_S_preTP-omega[-1]).max(), (omega[10]-omega[-1]).max()
#
# Make array of initial guesses for labor supply and savings
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
# print 'diff btwn start and end b: ', (guesses_b[0]-guesses_b[-1]).max()
#
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
# b_mat = np.zeros((T + S, S, J))
# n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
# # print 'diff btwn start and end n: ', (guesses_n[0]-guesses_n[-1]).max()
#
# # find economic aggregates
K = np.zeros(T+S)
L = np.zeros(T+S)
K[0] = K0
K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(guesses_b[:T-1], K_params)
K[T:] = Kss
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(guesses_n[:T], L_params)
L[T:] = Lss
Y_params = (alpha, Z)
Y = firm.get_Y(K, L, Y_params)
r_params = (alpha, delta)
r[:T] = firm.get_r(Y[:T], K[:T], r_params)
# uncomment lines below if want to use starting values from prior run
r = TPI_START_VALUES['r']
K = TPI_START_VALUES['K']
L = TPI_START_VALUES['L']
Y = TPI_START_VALUES['Y']
T_H = TPI_START_VALUES['T_H']
BQ = TPI_START_VALUES['BQ']
G = TPI_START_VALUES['G']
guesses_b = TPI_START_VALUES['b_mat']
guesses_n = TPI_START_VALUES['n_mat']
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
# print 'analytical mtrs in tpi = ', analytical_mtrs
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
K_plot = list(K) + list(np.ones(10) * Kss)
L_plot = list(L) + list(np.ones(10) * Lss)
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), Kpath_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
guesses = (guesses_b, guesses_n)
w_params = (Z, alpha, delta)
w = firm.get_w_from_r(r, w_params)
# print 'r and rss diff = ', r-rss
# print 'w and wss diff = ', w-wss
# print 'BQ and BQss diff = ', BQ-BQss
# print 'T_H and T_Hss diff = ', T_H - T_Hss
# print 'guess b and bss = ', (bssmat_splus1 - guesses_b).max()
# print 'guess n and nss = ', (nssmat - guesses_n).max()
outer_loop_vars = (r, w, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
# print 'guess b and bss = ', (b_mat - guesses_b).max()
# print 'guess n and nss over time = ', (n_mat - guesses_n).max(axis=2).max(axis=1)
# print 'guess n and nss over age = ', (n_mat - guesses_n).max(axis=0).max(axis=1)
# print 'guess n and nss over ability = ', (n_mat - guesses_n).max(axis=0).max(axis=0)
# quit()
print 'Max Euler error: ', (np.abs(euler_errors)).max()
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
K[0] = K0
K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J),
imm_rates[:T-1].reshape(T-1, S, 1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(bmat_splus1[:T-1], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
# print 'K diffs = ', K-K0
# print 'L diffs = ', L-L[0]
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
wnew = firm.get_w_from_r(rnew, w_params)
omega_shift = np.append(omega_S_preTP.reshape(1, S),
omega[:T-1, :], axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J),
rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
# b_mat_shift = np.append(np.reshape(initial_b, (1, S, J)),
# b_mat[:T-1, :, :], axis=0)
b_mat_shift = bmat_splus1[:T, :, :]
# print 'b diffs = ', (bmat_splus1[100, :, :] - initial_b).max(), (bmat_splus1[0, :, :] - initial_b).max(), (bmat_splus1[1, :, :] - initial_b).max()
# print 'r diffs = ', rnew[1]-r[1], rnew[100]-r[100], rnew[-1]-r[-1]
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift,
BQ_params)
BQss2 = np.empty(J)
for j in range(J):
BQss_params = (omega[1, :], lambdas[j], rho, g_n_vector[1], 'SS')
BQss2[j] = household.get_BQ(rnew[1], bmat_splus1[1, :, j],
BQss_params)
# print 'BQ test = ', BQss2-BQss, BQss-BQnew[1], BQss-BQnew[100], BQss-BQnew[-1]
total_tax_params = np.zeros((T, S, J, etr_params.shape[2]))
for i in range(etr_params.shape[2]):
total_tax_params[:, :, :, i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_receipt_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
total_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
net_tax_receipts = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, tax_receipt_params)) + [T_Hss] * S)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
if fix_transfers:
T_H_new = T_H
G[:T] = net_tax_receipts[:T] - T_H[:T]
else:
T_H_new = net_tax_receipts
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
G[:T] = 0.0
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(
np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)),
lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
b_to_use = np.zeros((T, S, J))
b_to_use[0, 1:, :] = initial_b[:-1, :]
b_to_use[1:, 1:, :] = b_mat[:T-1, :-1, :]
tax_path = tax.total_taxes(
np.tile(r[:T].reshape(T, 1, 1),(1,S,J)),
np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), b_to_use,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor,
T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
y_path = (np.tile(r[:T].reshape(T, 1, 1), (1, S, J)) * b_to_use[:T, :, :] +
np.tile(w[:T].reshape(T, 1, 1), (1, S, J)) *
np.tile(e.reshape(1, S, J), (T, 1, 1)) * n_mat[:T, :, :])
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), b_to_use[:T,:,:], b_mat[:T,:,:], n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
if T_H.all() != 0:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
print 'r dist = ', np.array(list(utils.pct_diff_func(rnew[:T], r[:T]))).max()
print 'BQ dist = ', np.array(list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten())).max()
print 'T_H dist = ', np.array(list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
print 'T_H path = ', T_H[:20]
# print 'r old = ', r[:T]
# print 'r new = ', rnew[:T]
# print 'K old = ', K[:T]
# print 'L old = ', L[:T]
# print 'income = ', y_path[:, :, -1]
# print 'taxes = ', tax_path[:, :, -1]
# print 'labor supply = ', n_mat[:, :, -1]
# print 'max and min labor = ', n_mat.max(), n_mat.min()
# print 'max and min labor = ', np.argmax(n_mat), np.argmin(n_mat)
# print 'max and min labor, j = 7 = ', n_mat[:,:,-1].max(), n_mat[:,:,-1].min()
# print 'max and min labor, j = 6 = ', n_mat[:,:,-2].max(), n_mat[:,:,-2].min()
# print 'max and min labor, j = 5 = ', n_mat[:,:,4].max(), n_mat[:,:,4].min()
# print 'max and min labor, j = 4 = ', n_mat[:,:,3].max(), n_mat[:,:,3].min()
# print 'max and min labor, j = 3 = ', n_mat[:,:,2].max(), n_mat[:,:,2].min()
# print 'max and min labor, j = 2 = ', n_mat[:,:,1].max(), n_mat[:,:,1].min()
# print 'max and min labor, j = 1 = ', n_mat[:,:,0].max(), n_mat[:,:,0].min()
# print 'max and min labor, S = 80 = ', n_mat[:,-1,-1].max(), n_mat[:,-1,-1].min()
# print "number > 1 = ", (n_mat > 1).sum()
# print "number < 0, = ", (n_mat < 0).sum()
# print "number > 1, j=7 = ", (n_mat[:T,:,-1] > 1).sum()
# print "number < 0, j=7 = ", (n_mat[:T,:,-1] < 0).sum()
# print "number > 1, s=80, j=7 = ", (n_mat[:T,-1,-1] > 1).sum()
# print "number < 0, s=80, j=7 = ", (n_mat[:T,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 80= ", (n_mat[:T,-1,-1] > 1).sum()
# print "number < 0, j = 7, age 80= ", (n_mat[:T,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] > 1).sum()
# print "number < 0, j = 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] < 0).sum()
# print "number > 1, j= 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80,-1] > 1).sum()
# print "number < 0, j = 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80 ,-1] < 0).sum()
# diag_dict = {'n_mat': n_mat, 'b_mat': b_mat, 'y_path': y_path, 'c_path': c_path}
# pickle.dump(diag_dict, open('tpi_iter1.pkl', 'wb'))
else:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) +
list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
list(np.abs(T_H_new[:T]-T_H[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
Y[:T] = Ynew
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
K[0] = K0
K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
K[1:T] = household.get_K(bmat_splus1[:T-1], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
wnew = firm.get_w_from_r(rnew, w_params)
omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
total_tax_params = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
total_tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_receipt_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
total_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
net_tax_receipts = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, tax_receipt_params)) + [T_Hss] * S)
if fix_transfers:
G[:T] = net_tax_receipts[:T] - T_H[:T]
else:
T_H[:T] = net_tax_receipts[:T]
G[:T] = 0.0
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
I = firm.get_I(bmat_splus1[:T], K[1:T+1], K[:T], I_params)
rc_error = Y[:T] - C[:T] - I[:T] - G[:T]
print 'Resource Constraint Difference:', rc_error
# compute utility
u_params = (sigma, np.tile(chi_n.reshape(1, S, 1), (T, 1, J)),
b_ellipse, ltilde, upsilon,
np.tile(rho.reshape(1, S, 1), (T, 1, J)),
np.tile(chi_b.reshape(1, 1, J), (T, S, 1)))
utility_path = household.get_u(c_path[:T, :, :], n_mat[:T, :, :],
bmat_splus1[:T, :, :], u_params)
# compute before and after-tax income
y_path = (np.tile(r[:T].reshape(T, 1, 1), (1, S, J)) * bmat_s[:T, :, :] +
np.tile(w[:T].reshape(T, 1, 1), (1, S, J)) *
np.tile(e.reshape(1, S, J), (T, 1, 1)) * n_mat[:T, :, :])
inctax_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), etr_params_path)
y_aftertax_path = (y_path -
tax.tau_income(np.tile(r[:T].reshape(T, 1, 1), (1, S, J)),
np.tile(w[:T].reshape(T, 1, 1), (1, S, J)),
bmat_s[:T,:,:], n_mat[:T,:,:], factor, inctax_params))
# compute after-tax wealth
wtax_params = (h_wealth, p_wealth, m_wealth)
b_aftertax_path = bmat_s[:T,:,:] - tax.tau_wealth(bmat_s[:T,:,:], wtax_params)
print'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ, 'G': G,
'T_H': T_H, 'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
'c_path': c_path, 'tax_path': tax_path, 'bmat_s': bmat_s,
'utility_path': utility_path, 'b_aftertax_path': b_aftertax_path,
'y_aftertax_path': y_aftertax_path, 'y_path': y_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}
macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
'BQ': BQ, 'G': G, 'T_H': T_H, 'r': r, 'w': w,
'tax_path': tax_path}
# if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
# raise RuntimeError("Transition path equlibrium not found")
#
# if ((np.any(np.absolute(rc_error) >= 1e-6))
# and ENFORCE_SOLUTION_CHECKS):
# raise RuntimeError("Transition path equlibrium not found")
#
# if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
# (np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
# and ENFORCE_SOLUTION_CHECKS):
# raise RuntimeError("Transition path equlibrium not found")
return output, macro_output
def run_steady_state(ss_parameters,
iterative_params,
get_baseline=False,
calibrate_model=False,
output_dir="./OUTPUT"):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
if get_baseline:
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S + J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * .01
n_guess = np.ones((S, J)).flatten() * .5 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0
factorguess = 100000
solutions = SS_solver(b_guess.reshape(S,
J), n_guess.reshape(S,
J), wguess,
rguess, T_Hguess, factorguess, chi_params[J:],
chi_params[:J], ss_parameters, iterative_params,
tau_bq, rho, lambdas, omega_SS, e)
if calibrate_model:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_path = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_path, "wb"))
function_to_minimize_X = lambda x: function_to_minimize(
x, chi_params, ss_parameters, iterative_params, omega_SS, rho,
lambdas, tau_bq, e, output_dir)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S + J)
chi_params_scalars = opt.minimize(function_to_minimize_X,
chi_params_scalars,
method='TNC',
tol=MINIMIZER_TOL,
bounds=bnds,
options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J),
wguess, rguess, T_Hguess, factorguess,
chi_params[J:], chi_params[:J],
ss_parameters, iterative_params, tau_bq, rho,
lambdas, omega_SS, e)
else:
variables = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
chi_params = solutions_dict['chi_params']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
solutions = SS_solver(b_guess.reshape(S,
J), n_guess.reshape(S,
J), wguess,
rguess, T_Hguess, factorguess, chi_params[J:],
chi_params[:J], ss_parameters, iterative_params,
tau_bq, rho, lambdas, omega_SS, e)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_dir = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_dir, "wb"))
else:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_exp_dir = os.path.join(output_dir,
"Saved_moments/SS_experiment_solutions.pkl")
pickle.dump(outputs, open(ss_exp_dir, "wb"))
bssmat = solutions[0:(S - 1) * J].reshape(S - 1, J)
bq = solutions[(S - 1) * J:S * J]
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2 * S * J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2 * S * J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(S, 1), lambdas,
g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, e, J,
omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(S, 1),
lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas, factor_ss,
T_Hss, None, 'SS', False, ss_parameters, theta,
tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(1, J),
lambdas.reshape(1, J), bssmat_splus1,
ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
household.constraint_checker_SS(bssmat, nssmat, cssmat, ss_parameters)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(
list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(
wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
b_splus2[:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j],
ss_parameters, theta[j], tau_bq[j], rho, lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(
wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
BQss[j], factor_ss, T_Hss, chi_n, ss_parameters, theta[j],
tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {
'Kss': Kss,
'bssmat': bssmat,
'Lss': Lss,
'nssmat': nssmat,
'Yss': Yss,
'wss': wss,
'rss': rss,
'theta': theta,
'BQss': BQss,
'factor_ss': factor_ss,
'bssmat_s': bssmat_s,
'cssmat': cssmat,
'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss,
'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure,
'chi_n': chi_n,
'chi_b': chi_b
}
if get_baseline:
utils.mkdirs(os.path.join(output_dir, "SSinit"))
ss_init_dir = os.path.join(output_dir, "SSinit/ss_init_vars.pkl")
pickle.dump(output, open(ss_init_dir, "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init': bssmat_init, 'nssmat_init': nssmat_init}
ss_init_tpi = os.path.join(output_dir, "SSinit/ss_init_tpi_vars.pkl")
pickle.dump(output2, open(ss_init_tpi, "wb"))
else:
utils.mkdirs(os.path.join(output_dir, "SS"))
ss_vars = os.path.join(output_dir, "SS/ss_vars.pkl")
pickle.dump(output, open(ss_vars, "wb"))
return output
def SS_solver(b_guess_init, n_guess_init, wss, rss, T_Hss, factor_ss, params, fsolve_flag=False):
'''
--------------------------------------------------------------------
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using a bisection method similar to TPI.
--------------------------------------------------------------------
INPUTS:
b_guess_init = [S,J] array, initial guesses for savings
n_guess_init = [S,J] array, initial guesses for labor supply
wguess = scalar, initial guess for SS real wage rate
rguess = scalar, initial guess for SS real interest rate
T_Hguess = scalar, initial guess for lump sum transfer
factorguess = scalar, initial guess for scaling factor to dollars
chi_b = [J,] vector, chi^b_j, the utility weight on bequests
chi_n = [S,] vector, chi^n_s utility weight on labor supply
params = lenght X tuple, list of parameters
iterative_params = length X tuple, list of parameters that determine the convergence
of the while loop
tau_bq = [J,] vector, bequest tax rate
rho = [S,] vector, mortality rates by age
lambdas = [J,] vector, fraction of population with each ability type
omega = [S,] vector, stationary population weights
e = [S,J] array, effective labor units by age and ability type
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
euler_equation_solver()
household.get_K()
firm.get_L()
firm.get_Y()
firm.get_r()
firm.get_w()
household.get_BQ()
tax.replacement_rate_vals()
tax.get_lump_sum()
utils.convex_combo()
utils.pct_diff_func()
OBJECTS CREATED WITHIN FUNCTION:
b_guess = [S,] vector, initial guess at household savings
n_guess = [S,] vector, initial guess at household labor supply
b_s = [S,] vector, wealth enter period with
b_splus1 = [S,] vector, household savings
b_splus2 = [S,] vector, household savings one period ahead
BQ = scalar, aggregate bequests to lifetime income group
theta = scalar, replacement rate for social security benenfits
error1 = [S,] vector, errors from FOC for savings
error2 = [S,] vector, errors from FOC for labor supply
tax1 = [S,] vector, total income taxes paid
cons = [S,] vector, household consumption
RETURNS: solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
OUTPUT: None
--------------------------------------------------------------------
'''
bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params = params
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, tau_bq, rho, omega_SS, lambdas, e, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
chi_b, chi_n = chi_params
maxiter, mindist_SS = iterative_params
# Rename the inputs
w = wss
r = rss
T_H = T_Hss
factor = factor_ss
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, T_H and
# factor
outer_loop_vars = (bssmat, nssmat, r, w, T_H, factor)
inner_loop_params = (ss_params, income_tax_params, chi_params)
euler_errors, bssmat, nssmat, new_r, new_w, \
new_T_H, new_factor, new_BQ, average_income_model = inner_loop(outer_loop_vars, inner_loop_params)
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
T_H = utils.convex_combo(new_T_H, T_H, nu)
if T_H != 0:
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_T_H, T_H)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
# If T_H is zero (if there are no taxes), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[abs(new_T_H - T_H)] +
[utils.pct_diff_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
print 'New value of nu:', nu
iteration += 1
print "Iteration: %02d" % iteration, " Distance: ", dist
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
bssmat_s = np.append(np.zeros((1,J)),bssmat[:-1,:],axis=0)
bssmat_splus1 = bssmat
wss = w
rss = r
factor_ss = factor
T_Hss = T_H
Kss_params = (omega_SS.reshape(S, 1), lambdas, g_n_ss, 'SS')
Kss = household.get_K(bssmat_splus1, Kss_params)
Lss_params = (e, omega_SS.reshape(S, 1), lambdas, 'SS')
Lss = firm.get_L(nssmat, Lss_params)
Yss_params = (alpha, Z)
Yss = firm.get_Y(Kss, Lss, Yss_params)
Iss_params = (delta, g_y, g_n_ss)
Iss = firm.get_I(Kss, Kss, Iss_params)
BQss = new_BQ
theta = np.zeros(J) # zero out payroll taxes since included in tax functions
# # theta_params = (e, J, omega_SS.reshape(S, 1), lambdas)
# # tax.replacement_rate_vals(nssmat, wss, factor_ss, theta_params)
# solve resource constraint
etr_params_3D = np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1))
mtrx_params_3D = np.tile(np.reshape(mtrx_params,(S,1,mtrx_params.shape[1])),(1,J,1))
'''
------------------------------------------------------------------------
The code below is to calulate and save model MTRs
- only exists help debug
------------------------------------------------------------------------
'''
# etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
# etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
# mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
# mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
# e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
# nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
# mtry_ss_params = (e_extended[1:,:], etr_params_extended_3D, mtry_params_extended_3D, analytical_mtrs)
# mtry_ss = tax.MTR_capital(rss, wss, bssmat_splus1, nss_extended[1:,:], factor_ss, mtry_ss_params)
# mtrx_ss_params = (e, etr_params_3D, mtrx_params_3D, analytical_mtrs)
# mtrx_ss = tax.MTR_labor(rss, wss, bssmat_s, nssmat, factor_ss, mtrx_ss_params)
# np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
# np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
# solve resource constraint
taxss_params = (e, lambdas, 'SS', retire, etr_params_3D,
h_wealth, p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
taxss = tax.total_taxes(rss, wss, bssmat_s, nssmat, BQss, factor_ss, T_Hss, None, False, taxss_params)
css_params = (e, lambdas.reshape(1, J), g_y)
cssmat = household.get_cons(rss, wss, bssmat_s, bssmat_splus1, nssmat, BQss.reshape(
1, J), taxss, css_params)
Css_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css = household.get_C(cssmat, Css_params)
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
if ENFORCE_SOLUTION_CHECKS and np.absolute(resource_constraint) > 1e-8:
err = "Steady state aggregate resource constraint not satisfied"
raise RuntimeError(err)
# check constraints
household.constraint_checker_SS(bssmat, nssmat, cssmat, ltilde)
euler_savings = euler_errors[:S,:]
euler_labor_leisure = euler_errors[S:,:]
'''
------------------------------------------------------------------------
Return dictionary of SS results
------------------------------------------------------------------------
'''
output = {'Kss': Kss, 'bssmat': bssmat, 'Lss': Lss, 'Css':Css, 'nssmat': nssmat, 'Yss': Yss,
'wss': wss, 'rss': rss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss,
'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b}
return output
savings = np.copy(bssmat_splus1)
beq_ut = chi_b.reshape(S, J) * (rho.reshape(S, 1)) * \
(savings**(1 - sigma) - 1) / (1 - sigma)
utility = ((cssmat_init ** (1 - sigma) - 1) / (1 - sigma)) + chi_n.reshape(S, 1) * \
(b_ellipse * (1 - (nssmat_init / ltilde)**upsilon) ** (1 / upsilon) + k_ellipse)
utility += beq_ut
utility_init = utility.sum(0)
T_Hss_init = T_Hss
Kss_init = Kss
Lss_init = Lss
Css_init = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
iss_init = firm.get_I(bssmat_splus1, bssmat_splus1, delta, g_y, g_n_ss)
income_init = cssmat + iss_init
# print((income_init*omega_SS).sum())
# print(Css + delta * Kss)
# print(Kss)
# print(Lss)
# print(Css_init)
# print()(utility_init * omega_SS).sum())
the_inequalizer(income_init, omega_SS, lambdas, S, J)
'''
------------------------------------------------------------------------
SS baseline graphs
------------------------------------------------------------------------
'''
def run_steady_state(income_tax_parameters, ss_parameters, iterative_params, get_baseline=False, calibrate_model=False, output_dir="./OUTPUT"):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S + J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * 0.05
n_guess = np.ones((S, J)).flatten() * .4 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0.12
factorguess = 70000.0
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:], chi_params[:J],
income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag)
if calibrate_model:
global Nfeval, value_all, chi_params_all
Nfeval = 1
value_all = np.zeros((10000))
chi_params_all = np.zeros((S+J,10000))
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_path = os.path.join(
output_dir, "Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_path, "wb"))
function_to_minimize_X = lambda x: function_to_minimize(
x, chi_params, income_tax_parameters, ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S + J)
#chi_params_scalars = opt.minimize(function_to_minimize_X, chi_params_scalars,
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds, callback=callbackF(chi_params_scalars), options=MINIMIZER_OPTIONS).x
# chi_params_scalars = opt.minimize(function_to_minimize, chi_params_scalars,
# args=(chi_params, income_tax_parameters, ss_parameters, iterative_params,
# omega_SS, rho, lambdas, tau_bq, e, output_dir),
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds,
# callback=callbackF(chi_params_scalars,chi_params, income_tax_parameters,
# ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir),
# options=MINIMIZER_OPTIONS).x
chi_params_scalars = opt.minimize(function_to_minimize, chi_params_scalars,
args=(chi_params, income_tax_parameters, ss_parameters, iterative_params,
omega_SS, rho, lambdas, tau_bq, e, output_dir),
method='TNC', tol=MINIMIZER_TOL, bounds=bnds,
options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:], chi_params[:J],
income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_dir = os.path.join(
output_dir, "Saved_moments/SS_baseline_solutions.pkl")
pickle.dump(outputs, open(ss_init_dir, "wb"))
else:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_exp_dir = os.path.join(
output_dir, "Saved_moments/SS_reform_solutions.pkl")
pickle.dump(outputs, open(ss_exp_dir, "wb"))
bssmat = solutions[0:(S - 1) * J].reshape(S - 1, J)
bq = solutions[(S - 1) * J:S * J] # technically, this is just the intentional bequests - wealth of those with max age
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2 * S * J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2 * S * J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = np.zeros(J) #tax.replacement_rate_vals(
#nssmat, wss, factor_ss, e, J, omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
etr_params_3D = np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1))
mtrx_params_3D = np.tile(np.reshape(mtrx_params,(S,1,mtrx_params.shape[1])),(1,J,1))
etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
mtry_ss = tax.MTR_capital(rss, bssmat_splus1, wss, e_extended[1:,:], nss_extended[1:,:], factor_ss,
analytical_mtrs, etr_params_extended_3D, mtry_params_extended_3D)
mtrx_ss = tax.MTR_labor(rss, bssmat_s, wss, e, nssmat, factor_ss, analytical_mtrs, etr_params_3D, mtrx_params_3D)
#np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
#np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
taxss_params = (J,S, retire, np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1)),
h_wealth, p_wealth, m_wealth, tau_payroll)
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas,
factor_ss, T_Hss, None, 'SS', False, taxss_params, theta, tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(
1, J), lambdas.reshape(1, J), bssmat_splus1, ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
constraint_params = ltilde
household.constraint_checker_SS(bssmat, nssmat, cssmat, constraint_params)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
b_splus2[:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j], income_tax_parameters, ss_parameters,
theta[j], tau_bq[j], rho, lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j],
b_splus1[:, j], BQss[j], factor_ss, T_Hss, chi_n, income_tax_parameters,
ss_parameters, theta[j], tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {'Kss': Kss, 'bssmat': bssmat, 'Lss': Lss, 'Css':Css, 'nssmat': nssmat, 'Yss': Yss,
'wss': wss, 'rss': rss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss,
'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b}
utils.mkdirs(os.path.join(output_dir, "SSinit"))
ss_init_dir = os.path.join(output_dir, "SSinit/ss_init_vars.pkl")
pickle.dump(output, open(ss_init_dir, "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init': bssmat_init, 'nssmat_init': nssmat_init}
ss_init_tpi = os.path.join(output_dir, "SSinit/ss_init_tpi_vars.pkl")
pickle.dump(output2, open(ss_init_tpi, "wb"))
return output
Y_base = Yinit
BQpath_TPIbase = BQpath_TPI
eul_savings_init = eul_savings
eul_laborleisure_init = eul_laborleisure
b_mat_init = b_mat
n_mat_init = n_mat
T_H_initbase = T_H_init
b1 = np.zeros((T, S, J))
b1[:, 1:, :] = b_mat_init[:T, :-1, :]
b2 = np.zeros((T, S, J))
b2[:, :, :] = b_mat_init[:T, :, :]
c_path_init = c_path
inv_mat_init = firm.get_I(
b_mat_init[1:T + 1], b_mat_init[:T], delta, g_y, g_n_vector[:T].reshape(T, 1, 1))
y_mat_init = c_path_init + inv_mat_init
# Lifetime Utility Graphs:
c_ut_init = np.zeros((S, S, J))
for s in range(S - 1):
c_ut_init[:, s + 1, :] = c_path_init[s + 1:s + 1 + S, s + 1, :]
c_ut_init[:, 0, :] = c_path_init[:S, 0, :]
L_ut_init = np.zeros((S, S, J))
for s in range(S - 1):
L_ut_init[:, s + 1, :] = n_mat_init[s + 1:s + 1 + S, s + 1, :]
L_ut_init[:, 0, :] = n_mat_init[:S, 0, :]
B_ut_init = BQpath_TPIbase[S:T]
b_ut_init = np.zeros((S, S, J))
for s in range(S):
b_ut_init[:, s, :] = b_mat_init[s:s + S, s, :]
def SS_solver(b_guess_init, n_guess_init, wss, rss, T_Hss, BQss, factor_ss, params, baseline, fsolve_flag=False):
'''
--------------------------------------------------------------------
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using a bisection method similar to TPI.
--------------------------------------------------------------------
INPUTS:
b_guess_init = [S,J] array, initial guesses for savings
n_guess_init = [S,J] array, initial guesses for labor supply
wguess = scalar, initial guess for SS real wage rate
rguess = scalar, initial guess for SS real interest rate
T_Hguess = scalar, initial guess for lump sum transfer
factorguess = scalar, initial guess for scaling factor to dollars
chi_b = [J,] vector, chi^b_j, the utility weight on bequests
chi_n = [S,] vector, chi^n_s utility weight on labor supply
params = lenght X tuple, list of parameters
iterative_params = length X tuple, list of parameters that determine the convergence
of the while loop
tau_bq = [J,] vector, bequest tax rate
rho = [S,] vector, mortality rates by age
lambdas = [J,] vector, fraction of population with each ability type
omega = [S,] vector, stationary population weights
e = [S,J] array, effective labor units by age and ability type
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
euler_equation_solver()
household.get_K()
firm.get_L()
firm.get_Y()
firm.get_r()
firm.get_w()
household.get_BQ()
tax.replacement_rate_vals()
tax.get_lump_sum()
utils.convex_combo()
utils.pct_diff_func()
OBJECTS CREATED WITHIN FUNCTION:
b_guess = [S,] vector, initial guess at household savings
n_guess = [S,] vector, initial guess at household labor supply
b_s = [S,] vector, wealth enter period with
b_splus1 = [S,] vector, household savings
b_splus2 = [S,] vector, household savings one period ahead
BQ = scalar, aggregate bequests to lifetime income group
theta = scalar, replacement rate for social security benenfits
error1 = [S,] vector, errors from FOC for savings
error2 = [S,] vector, errors from FOC for labor supply
tax1 = [S,] vector, total income taxes paid
cons = [S,] vector, household consumption
RETURNS: solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
OUTPUT: None
--------------------------------------------------------------------
'''
bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params = params
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, tau_bq, rho, omega_SS, lambdas, imm_rates, e, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
chi_b, chi_n = chi_params
maxiter, mindist_SS = iterative_params
# Rename the inputs
w = wss
r = rss
T_H = T_Hss
BQ = BQss
factor = factor_ss
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, T_H and
# factor
outer_loop_vars = (bssmat, nssmat, r, w, T_H, factor)
inner_loop_params = (ss_params, income_tax_params, chi_params)
euler_errors, bssmat, nssmat, new_r, new_w, \
new_T_H, new_BQ, new_theta, new_factor, average_income_model = inner_loop(outer_loop_vars, inner_loop_params, baseline)
# print 'T_H: ', T_H, new_T_H
# print 'factor: ', factor, new_factor
# print 'interest rate: ', r, new_r
# print 'wage rate: ', w, new_w
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
T_H = utils.convex_combo(new_T_H, T_H, nu)
BQ = utils.convex_combo(new_BQ, BQ, nu)
theta = utils.convex_combo(new_theta, theta, nu)
if T_H != 0:
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[utils.pct_diff_func(new_T_H, T_H)] +
[utils.pct_diff_func(new_BQ, BQ)] +
[utils.pct_diff_func(new_theta, theta)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
# If T_H is zero (if there are no taxes), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array([utils.pct_diff_func(new_r, r)] +
[utils.pct_diff_func(new_w, w)] +
[abs(new_T_H - T_H)] +
[utils.pct_diff_func(new_BQ, BQ)] +
[utils.pct_diff_func(new_theta, theta)] +
[utils.pct_diff_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
#print 'New value of nu:', nu
iteration += 1
#print "Iteration: %02d" % iteration, " Distance: ", dist
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
bssmat_s = np.append(np.zeros((1,J)),bssmat[:-1,:],axis=0)
bssmat_splus1 = bssmat
wss = w
rss = r
factor_ss = factor
T_Hss = T_H
Kss_params = (omega_SS.reshape(S, 1), lambdas, imm_rates, g_n_ss, 'SS')
Kss = household.get_K(bssmat_splus1, Kss_params)
Lss_params = (e, omega_SS.reshape(S, 1), lambdas, 'SS')
Lss = firm.get_L(nssmat, Lss_params)
Yss_params = (alpha, Z)
Yss = firm.get_Y(Kss, Lss, Yss_params)
Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
Iss = firm.get_I(bssmat_splus1, Kss, Kss, Iss_params)
BQss = new_BQ
# theta_params = (e, S, J, omega_SS.reshape(S, 1), lambdas,retire)
# theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, theta_params)
# solve resource constraint
etr_params_3D = np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1))
mtrx_params_3D = np.tile(np.reshape(mtrx_params,(S,1,mtrx_params.shape[1])),(1,J,1))
taxss_params = (e, lambdas, 'SS', retire, etr_params_3D,
h_wealth, p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
taxss = tax.total_taxes(rss, wss, bssmat_s, nssmat, BQss, factor_ss, T_Hss, None, False, taxss_params)
css_params = (e, lambdas.reshape(1, J), g_y)
cssmat = household.get_cons(rss, wss, bssmat_s, bssmat_splus1, nssmat, BQss.reshape(
1, J), taxss, css_params)
Css_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css = household.get_C(cssmat, Css_params)
resource_constraint = Yss - (Css + Iss)
'''
------------------------------------------------------------------------
The code below is to calulate and save model MTRs
- only exists to help debug
------------------------------------------------------------------------
'''
# etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
# etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
# mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
# mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
# e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
# nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
# mtry_ss_params = (e_extended[1:,:], etr_params_extended_3D, mtry_params_extended_3D, analytical_mtrs)
# mtry_ss = tax.MTR_capital(rss, wss, bssmat_splus1, nss_extended[1:,:], factor_ss, mtry_ss_params)
# mtrx_ss_params = (e, etr_params_3D, mtrx_params_3D, analytical_mtrs)
# mtrx_ss = tax.MTR_labor(rss, wss, bssmat_s, nssmat, factor_ss, mtrx_ss_params)
#
# etr_ss_params = (e, etr_params_3D)
# etr_ss = tax.tau_income(rss, wss, bssmat_s, nssmat, factor_ss, etr_ss_params)
#
# np.savetxt("etr_ss.csv", etr_ss, delimiter=",")
# np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
# np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
print 'interest rate: ', rss
print 'wage rate: ', wss
print 'factor: ', factor_ss
print 'T_H', T_Hss
print 'Resource Constraint Difference:', resource_constraint
print 'Max Euler Error: ', (np.absolute(euler_errors)).max()
if ENFORCE_SOLUTION_CHECKS and np.absolute(resource_constraint) > 1e-8:
err = "Steady state aggregate resource constraint not satisfied"
raise RuntimeError(err)
# check constraints
household.constraint_checker_SS(bssmat, nssmat, cssmat, ltilde)
if np.absolute(resource_constraint) > 1e-8 or (np.absolute(euler_errors)).max() > 1e-8:
ss_flag = 1
else:
ss_flag = 0
euler_savings = euler_errors[:S,:]
euler_labor_leisure = euler_errors[S:,:]
'''
------------------------------------------------------------------------
Return dictionary of SS results
------------------------------------------------------------------------
'''
output = {'Kss': Kss, 'bssmat': bssmat, 'Lss': Lss, 'Css':Css, 'Iss':Iss,
'nssmat': nssmat, 'Yss': Yss,'wss': wss, 'rss': rss, 'theta': theta,
'BQss': BQss, 'factor_ss': factor_ss, 'bssmat_s': bssmat_s,
'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b, 'ss_flag':ss_flag}
return output
r_base = rinit
Y_base = Yinit
BQpath_TPIbase = BQpath_TPI
eul_savings_init = eul_savings
eul_laborleisure_init = eul_laborleisure
b_mat_init = b_mat
n_mat_init = n_mat
T_H_initbase = T_H_init
b1 = np.zeros((T, S, J))
b1[:, 1:, :] = b_mat_init[:T, :-1, :]
b2 = np.zeros((T, S, J))
b2[:, :, :] = b_mat_init[:T, :, :]
c_path_init = c_path
inv_mat_init = firm.get_I(b_mat_init[1:T + 1], b_mat_init[:T], delta, g_y,
g_n_vector[:T].reshape(T, 1, 1))
y_mat_init = c_path_init + inv_mat_init
# Lifetime Utility Graphs:
c_ut_init = np.zeros((S, S, J))
for s in range(S - 1):
c_ut_init[:, s + 1, :] = c_path_init[s + 1:s + 1 + S, s + 1, :]
c_ut_init[:, 0, :] = c_path_init[:S, 0, :]
L_ut_init = np.zeros((S, S, J))
for s in range(S - 1):
L_ut_init[:, s + 1, :] = n_mat_init[s + 1:s + 1 + S, s + 1, :]
L_ut_init[:, 0, :] = n_mat_init[:S, 0, :]
B_ut_init = BQpath_TPIbase[S:T]
b_ut_init = np.zeros((S, S, J))
for s in range(S):
b_ut_init[:, s, :] = b_mat_init[s:s + S, s, :]
def TP_solutions(winit, rinit, T_H_init, BQinit2, Kss, Lss, Yss, BQss, theta, income_tax_params, wealth_tax_params, ellipse_params, parameters, g_n_vector,
omega_stationary, K0, b_sinit, b_splus1init, L0, Y0, r0, BQ0,
T_H_0, tax0, c0, initial_b, initial_n, factor_ss, tau_bq, chi_b,
chi_n, output_dir="./OUTPUT", **kwargs):
'''
This function returns the solutions for all variables along the time path.
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
print 'Computing final solutions'
# Extend time paths past T
winit = np.array(list(winit) + list(np.ones(S) * wss))
rinit = np.array(list(rinit) + list(np.ones(S) * rss))
T_H_init = np.array(list(T_H_init) + list(np.ones(S) * T_Hss))
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(BQinit2[:,j]) + [BQss[j]] * S
BQinit = np.array(BQinit)
# Make array of initial guesses
domain = np.linspace(0, T, T)
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
# initialize array of Euler errors
euler_errors = np.zeros((T, 2 * S, J))
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss,
j, income_tax_params, parameters, theta, tau_bq), xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
# initialize array of diagonal elements
length_diag = (np.diag(np.transpose(etr_params[:S,:,0]),S-(s+2))).shape[0]
etr_params_to_use = np.zeros((length_diag,etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag,mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag,mtry_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_to_use[:,i] = np.diag(np.transpose(etr_params[:S,:,i]),S-(s+2))
mtrx_params_to_use[:,i] = np.diag(np.transpose(mtrx_params[:S,:,i]),S-(s+2))
mtry_params_to_use[:,i] = np.diag(np.transpose(mtry_params[:S,:,i]),S-(s+2))
inc_tax_params_upper = (analytical_mtrs, etr_params_to_use, mtrx_params_to_use, mtry_params_to_use)
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, inc_tax_params_upper, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
# initialize array of diagonal elements
length_diag = (np.diag(np.transpose(etr_params[:,t:t+S,i]))).shape[0]
etr_params_to_use = np.zeros((length_diag,etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag,mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag,mtry_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_to_use[:,i] = np.diag(np.transpose(etr_params[:,t:t+S,i]))
mtrx_params_to_use[:,i] = np.diag(np.transpose(mtrx_params[:,t:t+S,i]))
mtry_params_to_use[:,i] = np.diag(np.transpose(mtry_params[:,t:t+S,i]))
inc_tax_params_TP = (analytical_mtrs, etr_params_to_use, mtrx_params_to_use, mtry_params_to_use)
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, inc_tax_params_TP, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, inc_tax_params_TP, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kinit = household.get_K(b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T], omega_stationary[
:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
# initialize array
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = J, S, retire, etr_params_path, h_wealth, p_wealth, m_wealth, tau_payroll
tax_path = tax.total_taxes(np.tile(rinit[:T].reshape(T, 1, 1),(1,S,J)), b_s, np.tile(winit[:T].reshape(T, 1, 1),(1,S,J)),
np.tile(e.reshape(1, S, J),(T,1,1)), n_mat[:T,:,:], BQinit[:T, :].reshape(T, 1, J), lambdas,
factor_ss, T_H_init[:T].reshape(T, 1, 1), None, 'TPI', False, tax_path_params, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T].reshape(T, 1, J), lambdas.reshape(1, 1, J), b_splus1, parameters, tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[
:T].reshape(T, S, 1), lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1],
Kpath_TPI[:T], delta, g_y, g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print'Checking time path for violations of constaints.'
hh_constraint_params = ltilde
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, hh_constraint_params)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Create the unstationarized versions of the paths of macro aggregates
------------------------------------------------------------------------
'''
# tvec = np.linspace(0, len(C_path), len(C_path))
# growth_path = np.exp(g_y*tvec)
# pop_path = np.zeros(len(C_path))
# for i in range(0,len(C_path)):
# pop_path[i] = np.exp(g_n_vector[:i].sum()) # note that this normalizes the pop in the initial period to one
# growth_pop_path = growth_path*pop_path
# C_ns_path = C_path * growth_pop_path
# K_ns_path = Kinit * growth_pop_path
# BQ_ns_path = growth_pop_path * BQinit[:T]
# L_ns_path = Linit * pop_path
# T_H_ns_path = T_H_init[:T] * growth_pop_path
# w_ns_path = winit*growth_path
# I_ns_path = I_path * growth_pop_path
# Y_ns_path = Y_path * growth_pop_path
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Kpath_TPI': Kpath_TPI, 'b_mat': b_mat, 'c_path': c_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI, 'n_mat': n_mat,
'rinit': rinit, 'Y_path': Y_path, 'T_H_init': T_H_init,
'tax_path': tax_path, 'winit': winit}
macro_output = {'Kpath_TPI': Kpath_TPI, 'C_path': C_path, 'I_path': I_path,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI,
'rinit': rinit, 'Y_path': Y_path, 'T_H_init': T_H_init,
'winit': winit, 'tax_path': tax_path}
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_macro_vars.pkl")
pickle.dump(macro_output, open(tpi_vars, "wb"))
savings = np.copy(bssmat_splus1)
beq_ut = chi_b.reshape(S, J) * (rho.reshape(S, 1)) * \
(savings**(1 - sigma) - 1) / (1 - sigma)
utility = ((cssmat_init ** (1 - sigma) - 1) / (1 - sigma)) + chi_n.reshape(S, 1) * \
(b_ellipse * (1 - (nssmat_init / ltilde)**upsilon) ** (1 / upsilon) + k_ellipse)
utility += beq_ut
utility_init = utility.sum(0)
T_Hss_init = T_Hss
Kss_init = Kss
Lss_init = Lss
Css_init = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
iss_init = firm.get_I(bssmat_splus1, bssmat_splus1, delta, g_y, g_n_ss)
income_init = cssmat + iss_init
# print((income_init*omega_SS).sum())
# print(Css + delta * Kss)
# print(Kss)
# print(Lss)
# print(Css_init)
# print()(utility_init * omega_SS).sum())
the_inequalizer(income_init, omega_SS, lambdas, S, J)
'''
------------------------------------------------------------------------
SS baseline graphs
------------------------------------------------------------------------
'''
domain = np.linspace(starting_age, ending_age, S)
beq_ut = chi_b.reshape(1, J) * (rho.reshape(S, 1)) * \
(savings**(1 - sigma) - 1) / (1 - sigma)
utility = ((cssmat_init ** (1 - sigma) - 1) / (1 - sigma)) + chi_n.reshape(S, 1) * \
(b_ellipse * (1 - (nssmat_init / ltilde)**upsilon) ** (1 / upsilon) + k_ellipse)
utility += beq_ut
utility_init = utility.sum(0)
T_Hss_init = T_Hss
Kss_init = Kss
Lss_init = Lss
c_params = (omega_SS.reshape(S, 1), lambdas, 'SS')
Css_init = household.get_C(cssmat, c_params)
i_params = (delta, g_y, omega_SS.reshape(1,
S), lambdas, imm_rates, g_n_ss, 'SS')
iss_init = firm.get_I(bssmat_splus1, Kss_init, Kss_init, i_params)
income_init = cssmat + iss_init
# print (income_init*omega_SS).sum()
# print Css + delta * Kss
# print Kss
# print Lss
# print Css_init
# print (utility_init * omega_SS).sum()
the_inequalizer(income_init, omega_SS, lambdas, S, J)
'''
------------------------------------------------------------------------
SS baseline graphs
------------------------------------------------------------------------
'''
domain = np.linspace(starting_age, ending_age, S)