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 create_tpi_params(**sim_params):
'''
------------------------------------------------------------------------
Set factor and initial capital stock to SS from baseline
------------------------------------------------------------------------
'''
baseline_ss = os.path.join(sim_params['baseline_dir'], "SS/SS_vars.pkl")
ss_baseline_vars = pickle.load(open(baseline_ss, "rb"))
factor = ss_baseline_vars['factor_ss']
#initial_b = ss_baseline_vars['bssmat_s'] + ss_baseline_vars['BQss']/lambdas
initial_b = ss_baseline_vars['bssmat_splus1']
initial_n = ss_baseline_vars['nssmat']
SS_values = (ss_baseline_vars['Kss'],ss_baseline_vars['Lss'], ss_baseline_vars['rss'],
ss_baseline_vars['wss'], ss_baseline_vars['BQss'], ss_baseline_vars['T_Hss'],
ss_baseline_vars['bssmat_splus1'], ss_baseline_vars['nssmat'])
# Make a vector of all one dimensional parameters, to be used in the
# following functions
wealth_tax_params = [sim_params['h_wealth'], sim_params['p_wealth'], sim_params['m_wealth']]
ellipse_params = [sim_params['b_ellipse'], sim_params['upsilon']]
chi_params = [sim_params['chi_b_guess'], sim_params['chi_n_guess']]
N_tilde = sim_params['omega'].sum(1) #this should just be one in each year given how we've constructed omega
sim_params['omega'] = sim_params['omega'] / N_tilde.reshape(sim_params['T'] + sim_params['S'], 1)
tpi_params = [sim_params['J'], sim_params['S'], sim_params['T'], sim_params['BQ_dist'], sim_params['BW'],
sim_params['beta'], sim_params['sigma'], sim_params['alpha'],
sim_params['Z'], sim_params['delta'], sim_params['ltilde'],
sim_params['nu'], sim_params['g_y'], sim_params['g_n_vector'],
sim_params['tau_payroll'], sim_params['tau_bq'], sim_params['rho'], sim_params['omega'], N_tilde,
sim_params['lambdas'], sim_params['e'], sim_params['retire'], sim_params['mean_income_data'], factor] + \
wealth_tax_params + ellipse_params + chi_params
iterative_params = [sim_params['maxiter'], sim_params['mindist_SS'], sim_params['mindist_TPI']]
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
## Assumption for tax functions is that policy in last year of BW is
# extended permanently
etr_params_TP = np.zeros((S,T+S,sim_params['etr_params'].shape[2]))
etr_params_TP[:,:BW,:] = sim_params['etr_params']
etr_params_TP[:,BW:,:] = np.reshape(sim_params['etr_params'][:,BW-1,:],(S,1,sim_params['etr_params'].shape[2]))
mtrx_params_TP = np.zeros((S,T+S,sim_params['mtrx_params'].shape[2]))
mtrx_params_TP[:,:BW,:] = sim_params['mtrx_params']
mtrx_params_TP[:,BW:,:] = np.reshape(sim_params['mtrx_params'][:,BW-1,:],(S,1,sim_params['mtrx_params'].shape[2]))
mtry_params_TP = np.zeros((S,T+S,sim_params['mtry_params'].shape[2]))
mtry_params_TP[:,:BW,:] = sim_params['mtry_params']
mtry_params_TP[:,BW:,:] = np.reshape(sim_params['mtry_params'][:,BW-1,:],(S,1,sim_params['mtry_params'].shape[2]))
income_tax_params = (sim_params['analytical_mtrs'], etr_params_TP, mtrx_params_TP, mtry_params_TP)
'''
------------------------------------------------------------------------
Set other parameters and initial values
------------------------------------------------------------------------
'''
# Get an initial distribution of capital with the initial population
# distribution
K0_params = (omega[0].reshape(S, 1), lambdas, g_n_vector[0], 'SS')
K0 = household.get_K(initial_b, K0_params)
b_sinit = np.array(list(np.zeros(J).reshape(1, J)) + list(initial_b[:-1]))
b_splus1init = initial_b
L0_params = (e, omega[0].reshape(S, 1), lambdas, 'SS')
L0 = firm.get_L(initial_n, L0_params)
Y0_params = (alpha, Z)
Y0 = firm.get_Y(K0, L0, Y0_params)
w0 = firm.get_w(Y0, L0, alpha)
r0_params = (alpha, delta)
r0 = firm.get_r(Y0, K0, r0_params)
BQ0_params = (omega[0].reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r0, initial_b, BQ0_params)
theta_params = (e, J, omega[0].reshape(S, 1), lambdas)
theta = tax.replacement_rate_vals(initial_n, w0, factor, theta_params)
T_H_params = (e, BQ_dist, lambdas, omega[0].reshape(S, 1), 'SS', etr_params_TP[:,0,:],
theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
T_H_0 = tax.get_lump_sum(r0, w0, b_sinit, initial_n, BQ0, factor, T_H_params)
etr_params_3D = np.tile(np.reshape(etr_params_TP[:,0,:],(S,1,etr_params_TP.shape[2])),(1,J,1))
tax0_params = (e, BQ_dist, lambdas, 'SS', retire, etr_params_3D, h_wealth, p_wealth, m_wealth,
tau_payroll, theta, tau_bq, J, S)
tax0 = tax.total_taxes(r0, w0, b_sinit, initial_n, BQ0, factor, T_H_0, None, False, tax0_params)
c0_params = (e, BQ_dist, lambdas.reshape(1, J), g_y)
c0 = household.get_cons(omega[0].reshape(S, 1), r0, w0, b_sinit, b_splus1init, initial_n, BQ0.reshape(
1, J), tax0, c0_params)
initial_values = (K0, b_sinit, b_splus1init, L0, Y0,
w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n)
return (income_tax_params, tpi_params, iterative_params, initial_values, SS_values)
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 create_tpi_params(a_tax_income,
b_tax_income,
c_tax_income,
d_tax_income,
b_ellipse,
upsilon,
J,
S,
T,
beta,
sigma,
alpha,
Z,
delta,
ltilde,
nu,
g_y,
tau_payroll,
retire,
mean_income_data,
get_baseline=True,
input_dir="./OUTPUT",
**kwargs):
if get_baseline:
ss_init = os.path.join(input_dir, "SSinit/ss_init_vars.pkl")
variables = pickle.load(open(ss_init, "rb"))
for key in variables:
globals()[key] = variables[key]
else:
params_path = os.path.join(input_dir,
"Saved_moments/params_changed.pkl")
variables = pickle.load(open(params_path, "rb"))
for key in variables:
globals()[key] = variables[key]
var_path = os.path.join(input_dir, "SS/ss_vars.pkl")
variables = pickle.load(open(var_path, "rb"))
for key in variables:
globals()[key] = variables[key]
init_tpi_vars = os.path.join(input_dir, "SSinit/ss_init_tpi_vars.pkl")
variables = pickle.load(open(init_tpi_vars, "rb"))
for key in variables:
globals()[key] = variables[key]
'''
------------------------------------------------------------------------
Set other parameters and initial values
------------------------------------------------------------------------
'''
# Make a vector of all one dimensional parameters, to be used in the
# following functions
income_tax_params = [
a_tax_income, b_tax_income, c_tax_income, d_tax_income
]
wealth_tax_params = [h_wealth, p_wealth, m_wealth]
ellipse_params = [b_ellipse, upsilon]
parameters = [
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss,
tau_payroll, retire, mean_income_data
] + income_tax_params + wealth_tax_params + ellipse_params
N_tilde = omega.sum(1)
omega_stationary = omega / N_tilde.reshape(T + S, 1)
if get_baseline:
initial_b = bssmat_splus1
initial_n = nssmat
else:
initial_b = bssmat_init
initial_n = nssmat_init
# Get an initial distribution of capital with the initial population
# distribution
K0 = household.get_K(initial_b, omega_stationary[0].reshape(S, 1), lambdas,
g_n_vector[0], 'SS')
b_sinit = np.array(list(np.zeros(J).reshape(1, J)) + list(initial_b[:-1]))
b_splus1init = initial_b
L0 = firm.get_L(e, initial_n, omega_stationary[0].reshape(S, 1), lambdas,
'SS')
Y0 = firm.get_Y(K0, L0, parameters)
w0 = firm.get_w(Y0, L0, parameters)
r0 = firm.get_r(Y0, K0, parameters)
BQ0 = household.get_BQ(r0, initial_b, omega_stationary[0].reshape(S, 1),
lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
T_H_0 = tax.get_lump_sum(r0, b_sinit, w0, e, initial_n, BQ0, lambdas,
factor_ss, omega_stationary[0].reshape(S, 1),
'SS', parameters, theta, tau_bq)
tax0 = tax.total_taxes(r0, b_sinit, w0, e, initial_n, BQ0, lambdas,
factor_ss, T_H_0, None, 'SS', False, parameters,
theta, tau_bq)
c0 = household.get_cons(r0, b_sinit, w0, e, initial_n, BQ0.reshape(1, J),
lambdas.reshape(1, J), b_splus1init, parameters,
tax0)
return (income_tax_params, wealth_tax_params, ellipse_params, parameters,
N_tilde, omega_stationary, K0, b_sinit, b_splus1init, L0, Y0, w0,
r0, BQ0, T_H_0, tax0, c0, initial_b, initial_n)
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 create_tpi_params(**sim_params):
'''
------------------------------------------------------------------------
Set factor and initial capital stock to SS from baseline
------------------------------------------------------------------------
'''
baseline_ss = os.path.join(sim_params['baseline_dir'], "SS/SS_vars.pkl")
ss_baseline_vars = pickle.load(open(baseline_ss, "rb"))
factor = ss_baseline_vars['factor_ss']
#initial_b = ss_baseline_vars['bssmat_s'] + ss_baseline_vars['BQss']/lambdas
initial_b = ss_baseline_vars['bssmat_splus1']
initial_n = ss_baseline_vars['nssmat']
SS_values = (ss_baseline_vars['Kss'], ss_baseline_vars['Lss'],
ss_baseline_vars['rss'], ss_baseline_vars['wss'],
ss_baseline_vars['BQss'], ss_baseline_vars['T_Hss'],
ss_baseline_vars['bssmat_splus1'], ss_baseline_vars['nssmat'])
# Make a vector of all one dimensional parameters, to be used in the
# following functions
wealth_tax_params = [
sim_params['h_wealth'], sim_params['p_wealth'], sim_params['m_wealth']
]
ellipse_params = [sim_params['b_ellipse'], sim_params['upsilon']]
chi_params = [sim_params['chi_b_guess'], sim_params['chi_n_guess']]
N_tilde = sim_params['omega'].sum(
1
) #this should just be one in each year given how we've constructed omega
sim_params['omega'] = sim_params['omega'] / N_tilde.reshape(
sim_params['T'] + sim_params['S'], 1)
tpi_params = [sim_params['J'], sim_params['S'], sim_params['T'], sim_params['BW'],
sim_params['beta'], sim_params['sigma'], sim_params['alpha'],
sim_params['Z'], sim_params['delta'], sim_params['ltilde'],
sim_params['nu'], sim_params['g_y'], sim_params['g_n_vector'],
sim_params['tau_payroll'], sim_params['tau_bq'], sim_params['rho'], sim_params['omega'], N_tilde,
sim_params['lambdas'], sim_params['e'], sim_params['retire'], sim_params['mean_income_data'], factor] + \
wealth_tax_params + ellipse_params + chi_params
iterative_params = [
sim_params['maxiter'], sim_params['mindist_SS'],
sim_params['mindist_TPI']
]
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
## Assumption for tax functions is that policy in last year of BW is
# extended permanently
etr_params_TP = np.zeros((S, T + S, sim_params['etr_params'].shape[2]))
etr_params_TP[:, :BW, :] = sim_params['etr_params']
etr_params_TP[:, BW:, :] = np.reshape(
sim_params['etr_params'][:, BW - 1, :],
(S, 1, sim_params['etr_params'].shape[2]))
mtrx_params_TP = np.zeros((S, T + S, sim_params['mtrx_params'].shape[2]))
mtrx_params_TP[:, :BW, :] = sim_params['mtrx_params']
mtrx_params_TP[:, BW:, :] = np.reshape(
sim_params['mtrx_params'][:, BW - 1, :],
(S, 1, sim_params['mtrx_params'].shape[2]))
mtry_params_TP = np.zeros((S, T + S, sim_params['mtry_params'].shape[2]))
mtry_params_TP[:, :BW, :] = sim_params['mtry_params']
mtry_params_TP[:, BW:, :] = np.reshape(
sim_params['mtry_params'][:, BW - 1, :],
(S, 1, sim_params['mtry_params'].shape[2]))
income_tax_params = (sim_params['analytical_mtrs'], etr_params_TP,
mtrx_params_TP, mtry_params_TP)
'''
------------------------------------------------------------------------
Set other parameters and initial values
------------------------------------------------------------------------
'''
# Get an initial distribution of capital with the initial population
# distribution
K0_params = (omega[0].reshape(S, 1), lambdas, g_n_vector[0], 'SS')
K0 = household.get_K(initial_b, K0_params)
b_sinit = np.array(list(np.zeros(J).reshape(1, J)) + list(initial_b[:-1]))
b_splus1init = initial_b
L0_params = (e, omega[0].reshape(S, 1), lambdas, 'SS')
L0 = firm.get_L(initial_n, L0_params)
Y0_params = (alpha, Z)
Y0 = firm.get_Y(K0, L0, Y0_params)
w0 = firm.get_w(Y0, L0, alpha)
r0_params = (alpha, delta)
r0 = firm.get_r(Y0, K0, r0_params)
BQ0_params = (omega[0].reshape(S, 1), lambdas, rho.reshape(S, 1),
g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r0, initial_b, BQ0_params)
theta_params = (e, J, omega[0].reshape(S, 1), lambdas)
theta = tax.replacement_rate_vals(initial_n, w0, factor, theta_params)
T_H_params = (e, lambdas, omega[0].reshape(S, 1), 'SS',
etr_params_TP[:, 0, :], theta, tau_bq, tau_payroll, h_wealth,
p_wealth, m_wealth, retire, T, S, J)
T_H_0 = tax.get_lump_sum(r0, w0, b_sinit, initial_n, BQ0, factor,
T_H_params)
etr_params_3D = np.tile(
np.reshape(etr_params_TP[:, 0, :], (S, 1, etr_params_TP.shape[2])),
(1, J, 1))
tax0_params = (e, lambdas, 'SS', retire, etr_params_3D, h_wealth, p_wealth,
m_wealth, tau_payroll, theta, tau_bq, J, S)
tax0 = tax.total_taxes(r0, w0, b_sinit, initial_n, BQ0, factor, T_H_0,
None, False, tax0_params)
c0_params = (e, lambdas.reshape(1, J), g_y)
c0 = household.get_cons(r0, w0, b_sinit, b_splus1init, initial_n,
BQ0.reshape(1, J), tax0, c0_params)
initial_values = (K0, b_sinit, b_splus1init, L0, Y0, w0, r0, BQ0, T_H_0,
factor, tax0, c0, initial_b, initial_n)
return (income_tax_params, tpi_params, iterative_params, initial_values,
SS_values)
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 create_tpi_params(a_tax_income, b_tax_income, c_tax_income,
d_tax_income,
b_ellipse, upsilon, J, S, T, beta, sigma, alpha, Z,
delta, ltilde, nu, g_y, tau_payroll, retire,
mean_income_data, get_baseline=True, input_dir="./OUTPUT", **kwargs):
if get_baseline:
ss_init = os.path.join(input_dir, "SSinit/ss_init_vars.pkl")
variables = pickle.load(open(ss_init, "rb"))
for key in variables:
globals()[key] = variables[key]
else:
params_path = os.path.join(
input_dir, "Saved_moments/params_changed.pkl")
variables = pickle.load(open(params_path, "rb"))
for key in variables:
globals()[key] = variables[key]
var_path = os.path.join(input_dir, "SS/ss_vars.pkl")
variables = pickle.load(open(var_path, "rb"))
for key in variables:
globals()[key] = variables[key]
init_tpi_vars = os.path.join(input_dir, "SSinit/ss_init_tpi_vars.pkl")
variables = pickle.load(open(init_tpi_vars, "rb"))
for key in variables:
globals()[key] = variables[key]
'''
------------------------------------------------------------------------
Set other parameters and initial values
------------------------------------------------------------------------
'''
# Make a vector of all one dimensional parameters, to be used in the
# following functions
income_tax_params = [a_tax_income,
b_tax_income, c_tax_income, d_tax_income]
wealth_tax_params = [h_wealth, p_wealth, m_wealth]
ellipse_params = [b_ellipse, upsilon]
parameters = [J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire,
mean_income_data] + income_tax_params + wealth_tax_params + ellipse_params
N_tilde = omega.sum(1)
omega_stationary = omega / N_tilde.reshape(T + S, 1)
if get_baseline:
initial_b = bssmat_splus1
initial_n = nssmat
else:
initial_b = bssmat_init
initial_n = nssmat_init
# Get an initial distribution of capital with the initial population
# distribution
K0 = household.get_K(initial_b, omega_stationary[
0].reshape(S, 1), lambdas, g_n_vector[0], 'SS')
b_sinit = np.array(list(np.zeros(J).reshape(1, J)) + list(initial_b[:-1]))
b_splus1init = initial_b
L0 = firm.get_L(e, initial_n, omega_stationary[
0].reshape(S, 1), lambdas, 'SS')
Y0 = firm.get_Y(K0, L0, parameters)
w0 = firm.get_w(Y0, L0, parameters)
r0 = firm.get_r(Y0, K0, parameters)
BQ0 = household.get_BQ(r0, initial_b, omega_stationary[0].reshape(
S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
T_H_0 = tax.get_lump_sum(r0, b_sinit, w0, e, initial_n, BQ0, lambdas, factor_ss, omega_stationary[
0].reshape(S, 1), 'SS', parameters, theta, tau_bq)
tax0 = tax.total_taxes(r0, b_sinit, w0, e, initial_n, BQ0, lambdas,
factor_ss, T_H_0, None, 'SS', False, parameters, theta, tau_bq)
c0 = household.get_cons(r0, b_sinit, w0, e, initial_n, BQ0.reshape(
1, J), lambdas.reshape(1, J), b_splus1init, parameters, tax0)
return (income_tax_params, wealth_tax_params, ellipse_params, parameters,
N_tilde, omega_stationary, K0, b_sinit, b_splus1init, L0, Y0,
w0, r0, BQ0, T_H_0, tax0, c0, initial_b, initial_n)
def create_tpi_params(analytical_mtrs, etr_params, mtrx_params, mtry_params,
b_ellipse, upsilon, J, S, T, BW, beta, sigma, alpha, Z,
delta, ltilde, nu, g_y, tau_payroll, retire,
mean_income_data, run_params,
input_dir="./OUTPUT", baseline_dir="./OUTPUT", **kwargs):
globals().update(run_params)
ss_init = os.path.join(input_dir, "SSinit/ss_init_vars.pkl")
variables = pickle.load(open(ss_init, "rb"))
for key in variables:
globals()[key] = variables[key]
'''
------------------------------------------------------------------------
Set factor and initial capital stock to SS from baseline
------------------------------------------------------------------------
'''
baseline_ss = os.path.join(baseline_dir, "SSinit/ss_init_vars.pkl")
ss_baseline_vars = pickle.load(open(baseline_ss, "rb"))
factor = ss_baseline_vars['factor_ss']
#initial_b = ss_baseline_vars['bssmat_s'] + ss_baseline_vars['BQss']/lambdas
initial_b= ss_baseline_vars['bssmat_splus1']
'''
------------------------------------------------------------------------
Set other parameters and initial values
------------------------------------------------------------------------
'''
# Make a vector of all one dimensional parameters, to be used in the
# following functions
# Put income tax parameters in a tuple
# Assumption here is that tax parameters of last year of budget
# window continue forever and so will be SS values
etr_params_TP = np.zeros((S,T+S,etr_params.shape[2]))
etr_params_TP[:,:BW,:] = etr_params
etr_params_TP[:,BW:,:] = np.reshape(etr_params[:,BW-1,:],(S,1,etr_params.shape[2]))
mtrx_params_TP = np.zeros((S,T+S,mtrx_params.shape[2]))
mtrx_params_TP[:,:BW,:] = mtrx_params
mtrx_params_TP[:,BW:,:] = np.reshape(mtrx_params[:,BW-1,:],(S,1,mtrx_params.shape[2]))
mtry_params_TP = np.zeros((S,T+S,mtry_params.shape[2]))
mtry_params_TP[:,:BW,:] = mtry_params
mtry_params_TP[:,BW:,:] = np.reshape(mtry_params[:,BW-1,:],(S,1,mtry_params.shape[2]))
income_tax_params = (analytical_mtrs, etr_params_TP, mtrx_params_TP, mtry_params_TP)
wealth_tax_params = [h_wealth, p_wealth, m_wealth]
ellipse_params = [b_ellipse, upsilon]
parameters = [J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire,
mean_income_data] + wealth_tax_params + ellipse_params
N_tilde = omega.sum(1)
omega_stationary = omega / N_tilde.reshape(T + S, 1)
initial_n = nssmat
# Get an initial distribution of capital with the initial population
# distribution
K0 = household.get_K(initial_b, omega_stationary[
0].reshape(S, 1), lambdas, g_n_vector[0], 'SS')
b_sinit = np.array(list(np.zeros(J).reshape(1, J)) + list(initial_b[:-1]))
b_splus1init = initial_b
L0 = firm.get_L(e, initial_n, omega_stationary[
0].reshape(S, 1), lambdas, 'SS')
Y0 = firm.get_Y(K0, L0, parameters)
w0 = firm.get_w(Y0, L0, parameters)
r0 = firm.get_r(Y0, K0, parameters)
BQ0 = household.get_BQ(r0, initial_b, omega_stationary[0].reshape(
S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
T_H_0 = tax.get_lump_sum(r0, b_sinit, w0, e, initial_n, BQ0, lambdas, factor_ss, omega_stationary[
0].reshape(S, 1), 'SS', etr_params_TP[:,0,:], parameters, theta, tau_bq)
tax0_params = (J, S, retire, np.tile(np.reshape(etr_params_TP[:,0,:],(S,1,etr_params_TP.shape[2])),(1,J,1)),
h_wealth, p_wealth, m_wealth, tau_payroll)
tax0 = tax.total_taxes(r0, b_sinit, w0, e, initial_n, BQ0, lambdas,
factor_ss, T_H_0, None, 'SS', False, tax0_params, theta, tau_bq)
c0 = household.get_cons(r0, b_sinit, w0, e, initial_n, BQ0.reshape(
1, J), lambdas.reshape(1, J), b_splus1init, parameters, tax0)
return (income_tax_params, wealth_tax_params, ellipse_params, parameters,
N_tilde, omega_stationary, K0, b_sinit, b_splus1init, L0, Y0,
w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n)
def run_time_path_iteration(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):
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
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)
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_init = np.ones(T + S) * T_Hss2
# 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, income_tax_params, 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))
# 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
# 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])
# 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))
# 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, :]
# initialize array
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_new = np.array(list(tax.get_lump_sum(np.tile(rnew.reshape(T, 1, 1),(1,S,J)), bmat_s, np.tile(wnew.reshape(
T, 1, 1),(1,S,J)), np.tile(e.reshape(1, S, J),(T,1,1)), n_mat[:T,:,:], BQnew.reshape(T, 1, J), lambdas.reshape(
1, 1, J), factor_ss, omega_stationary[:T].reshape(T, S, 1), 'TPI', TH_tax_params, parameters, theta, tau_bq)) + [T_Hss] * S)
winit[:T] = utils.convex_combo(wnew, winit[:T], nu)
rinit[:T] = utils.convex_combo(rnew, rinit[:T], nu)
BQinit[:T] = utils.convex_combo(BQnew, BQinit[:T], nu)
T_H_init[:T] = utils.convex_combo(T_H_new[:T], T_H_init[: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_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
return winit[:T], rinit[:T], T_H_init[:T], BQinit[:T], Yinit
def TPI_fsolve(guesses, 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):
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
# create full time paths with guesses and SS values
rinit = np.zeros(T+S)
winit = np.zeros(T+S)
T_H_init = np.zeros(T+S)
BQinit = np.zeros((T+S,J))
rinit[:T] = guesses[0:T].reshape(T)
winit[:T] = guesses[T:2*T].reshape(T)
rinit[T:] = rss
winit[T:] = wss
T_H_init[:T] = guesses[2*T:3*T].reshape(T)
BQinit[:T,:] = guesses[3*T:].reshape(T,J)
T_H_init[T:] = T_Hss
BQinit[T:,:] = BQss
# Make array of initial guesses for distribution of
# savings and labor supply
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)
euler_errors = np.zeros((T, 2 * S, J))
# Solve hh problem over time path:
# 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, income_tax_params, 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))
# 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
# 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])
# 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))
# 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')
# Plotting of Kpath and Lpath to check convergence
# make vectors of Kpath and Lpath to plot
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
TPI_FIG_DIR = output_dir
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"))
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, :]
# initialize array
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_new = np.array(list(tax.get_lump_sum(np.tile(rnew.reshape(T, 1, 1),(1,S,J)), bmat_s, np.tile(wnew.reshape(
T, 1, 1),(1,S,J)), np.tile(e.reshape(1, S, J),(T,1,1)), n_mat[:T,:,:], BQnew.reshape(T, 1, J), lambdas.reshape(
1, 1, J), factor_ss, omega_stationary[:T].reshape(T, S, 1), 'TPI', TH_tax_params, parameters, theta, tau_bq)) + [T_Hss] * S)
error1 = rinit[:T]-rnew[:T]
error2 = winit[:T]-wnew[:T]
error3 = T_H_init[:T]-T_H_new[:T]
error4 = BQinit[:T] - BQnew[:T]
# Check and punish constraing violations
mask1 = rinit[:T] <= 0
mask2 = winit[:T] <= 0
mask3 = np.isnan(rinit[:T])
mask4 = np.isnan(winit[:T])
error1[mask1] = 1e14
error2[mask2] = 1e14
error1[mask3] = 1e14
error2[mask4] = 1e14
mask5 = T_H_init[:T] < 0
mask6 = np.isnan(T_H_init[:T])
mask7 = BQinit[:T] < 0
mask8 = np.isnan(BQinit[:T])
error3[mask5] = 1e14
error3[mask6] = 1e14
error4[mask7] = 1e14
error4[mask8] = 1e14
errors = np.array(list(error1) +list(error2) + list(error3) + list(error4.flatten()))
print '\t\tDistance:', np.absolute(errors).max()
return errors
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_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
