def run_time_path_iteration(Kss,
Lss,
Yss,
BQss,
theta,
parameters,
g_n_vector,
omega_stationary,
K0,
b_sinit,
b_splus1init,
L0,
Y0,
r0,
BQ0,
T_H_0,
tax0,
c0,
initial_b,
initial_n,
factor_ss,
tau_bq,
chi_b,
chi_n,
get_baseline=False,
output_dir="./OUTPUT",
**kwargs):
TPI_FIG_DIR = output_dir
# Initialize Time paths
domain = np.linspace(0, T, T)
Kinit = (-1 / (domain + 1)) * (Kss - K0) + Kss
Kinit[-1] = Kss
Kinit = np.array(list(Kinit) + list(np.ones(S) * Kss))
Linit = np.ones(T + S) * Lss
Yinit = firm.get_Y(Kinit, Linit, parameters)
winit = firm.get_w(Yinit, Linit, parameters)
rinit = firm.get_r(Yinit, Kinit, parameters)
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQinit = np.array(BQinit)
T_H_init = np.ones(T + S) * T_Hss
# Make array of initial guesses
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
Kpath_TPI = list(Kinit) + list(np.ones(10) * Kss)
Lpath_TPI = list(Linit) + list(np.ones(10) * Lss)
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
plt.figure()
plt.axhline(y=Kss,
color='black',
linewidth=2,
label=r"Steady State $\hat{K}$",
ls='--')
plt.plot(np.arange(T + 10),
Kpath_TPI[:T + 10],
'b',
linewidth=2,
label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(
opt.fsolve(SS_TPI_firstdoughnutring,
[guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1],
initial_b, factor_ss, j, parameters, theta,
tau_bq),
xtol=1e-13))
# if np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1, j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq)).max() > 1e-6:
# print 'minidoughnut:',
# np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1,
# j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b,
# factor_ss, j, parameters, theta, tau_bq)).max()
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j],
S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j,
s, 0, parameters, theta, tau_bq, rho, lambdas, e,
initial_b, chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
# if abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n))).max() > 1e-6:
# print 's-loop:',
# abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit,
# BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters,
# theta, tau_bq, rho, lambdas, e, initial_b, chi_b,
# chi_n))).max()
for t in xrange(0, T):
b_guesses_to_use = .75 * \
np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j,
None, t, parameters, theta, tau_bq, rho, lambdas, e,
None, chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(
Steady_state_TPI_solver(inputs, winit, rinit, BQinit[:, j],
T_H_init, factor_ss, j, None, t,
parameters, theta, tau_bq, rho,
lambdas, e, None, chi_b, chi_n))
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
Kinit = household.get_K(b_mat[:T],
omega_stationary[:T].reshape(T, S, 1),
lambdas.reshape(1, 1,
J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T],
omega_stationary[:T, :].reshape(T, S, 1),
lambdas.reshape(1, 1, J), 'TPI')
Ynew = firm.get_Y(Kinit, Linit, parameters)
wnew = firm.get_w(Ynew, Linit, parameters)
rnew = firm.get_r(Ynew, Kinit, parameters)
# the following needs a g_n term
BQnew = household.get_BQ(rnew.reshape(T, 1), b_mat[:T],
omega_stationary[:T].reshape(T, S, 1),
lambdas.reshape(1, 1,
J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
T_H_new = np.array(
list(
tax.get_lump_sum(
rnew.reshape(T, 1, 1), bmat_s, wnew.reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T], BQnew.reshape(T, 1, J),
lambdas.reshape(1, 1, J), factor_ss, omega_stationary[:T].
reshape(T, S, 1), 'TPI', parameters, theta, tau_bq)) +
[T_Hss] * S)
winit[:T] = utils.convex_combo(wnew, winit[:T], parameters)
rinit[:T] = utils.convex_combo(rnew, rinit[:T], parameters)
BQinit[:T] = utils.convex_combo(BQnew, BQinit[:T], parameters)
T_H_init[:T] = utils.convex_combo(T_H_new[:T], T_H_init[:T],
parameters)
guesses_b = utils.convex_combo(b_mat, guesses_b, parameters)
guesses_n = utils.convex_combo(n_mat, guesses_n, parameters)
if T_H_init.all() != 0:
TPIdist = np.array(
list(utils.perc_dif_func(rnew, rinit[:T])) +
list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) +
list(utils.perc_dif_func(wnew, winit[:T])) +
list(utils.perc_dif_func(T_H_new, T_H_init))).max()
else:
TPIdist = np.array(
list(utils.perc_dif_func(rnew, rinit[:T])) +
list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) +
list(utils.perc_dif_func(wnew, winit[:T])) +
list(np.abs(T_H_new, T_H_init))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
if TPIiter > 10:
if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
nu /= 2
print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
print 'Computing final solutions'
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(
opt.fsolve(SS_TPI_firstdoughnutring,
[guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1],
initial_b, factor_ss, j, parameters, theta,
tau_bq),
xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0,
parameters, theta, tau_bq, rho, lambdas, e, initial_b,
chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(
Steady_state_TPI_solver,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None,
t, parameters, theta, tau_bq, rho, lambdas, e, None,
chi_b, chi_n),
xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(
Steady_state_TPI_solver(inputs, winit, rinit, BQinit[:, j],
T_H_init, factor_ss, j, None, t,
parameters, theta, tau_bq, rho,
lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
tax_path = tax.total_taxes(rinit[:T].reshape(T, 1, 1),
b_s, winit[:T].reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T],
BQinit[:T, :].reshape(T, 1, J), lambdas,
factor_ss, T_H_init[:T].reshape(T, 1, 1), None,
'TPI', False, parameters, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1),
b_s, winit[:T].reshape(T, 1, 1),
e.reshape(1, S, J), n_mat[:T],
BQinit[:T].reshape(T, 1, J),
lambdas.reshape(1, 1, J), b_splus1, parameters,
tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[:T].reshape(T, S, 1),
lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1], Kpath_TPI[:T], delta, g_y,
g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print 'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_path[t], t,
parameters)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Kpath_TPI': Kpath_TPI,
'b_mat': b_mat,
'c_path': c_path,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI,
'BQpath_TPI': BQpath_TPI,
'n_mat': n_mat,
'rinit': rinit,
'Yinit': Yinit,
'T_H_init': T_H_init,
'tax_path': tax_path,
'winit': winit
}
if get_baseline:
tpi_init_dir = os.path.join(output_dir, "TPIinit")
utils.mkdirs(tpi_init_dir)
tpi_init_vars = os.path.join(tpi_init_dir, "TPIinit_vars.pkl")
pickle.dump(output, open(tpi_init_vars, "wb"))
else:
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
def run_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 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 function_to_minimize(chi_params_scalars, chi_params_init, income_tax_parameters, ss_parameters,
iterative_params, weights_SS, rho_vec, lambdas, tau_bq, e, output_dir):
'''
Inputs:
chi_params_scalars = guesses for multipliers for chi parameters
((S+J)x1 array)
chi_params_init = chi parameters that will be multiplied
((S+J)x1 array)
params = list of parameters (list)
weights_SS = steady state population weights (Sx1 array)
rho_vec = mortality rates (Sx1 array)
lambdas = ability weights (Jx1 array)
tau_bq = bequest tax rates (Jx1 array)
e = ability levels (Jx1 array)
Output:
The sum of absolute percent deviations between the actual and
simulated wealth moments
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters
chi_params_init *= chi_params_scalars
# print 'Print Chi_b: ', chi_params_init[:J]
# print 'Scaling vals:', chi_params_scalars[:J]
ss_init_path = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:(S * J)]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[(2 * S * J):]
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params_init[J:], chi_params_init[:J],
income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params_init[
J:], chi_params_init[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag)
b_new = solutions[:(S * J)]
n_new = solutions[(S * J):(2 * S * J)]
w_new, r_new, factor_new, T_H_new = solutions[(2 * S * J):]
# Wealth Calibration Euler
error5 = list(utils.check_wealth_calibration(b_new.reshape(S, J)[:-1, :],
factor_new, ss_parameters, output_dir))
# labor calibration euler
labor_path = os.path.join(
output_dir, "Saved_moments/labor_data_moments.pkl")
lab_data_dict = pickle.load(open(labor_path, "rb"))
labor_sim = (n_new.reshape(S, J) * lambdas.reshape(1, J)).sum(axis=1)
if DATASET == 'SMALL':
lab_dist_data = lab_data_dict['labor_dist_data'][:S]
else:
lab_dist_data = lab_data_dict['labor_dist_data']
error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data))
# combine eulers
output = np.array(error5 + error6)
# Constraints
eul_error = np.ones(J)
for j in xrange(J):
eul_error[j] = np.abs(Euler_equation_solver(np.append(b_new.reshape(S, J)[:, j], n_new.reshape(S, J)[:, j]), r_new, w_new,
T_H_new, factor_new, j, income_tax_parameters, ss_parameters, chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas, weights_SS, e)).max()
fsolve_no_converg = eul_error.max()
if np.isnan(fsolve_no_converg):
fsolve_no_converg = 1e6
if fsolve_no_converg > 1e-4:
# If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be
# and don't save the solutions as initial guesses (since they might be
# gibberish)
output += 1e14
else:
var_names = ['solutions']
dictionary = {}
for key in var_names:
dictionary[key] = locals()[key]
ss_init_path = os.path.join(
output_dir, "Saved_moments/SS_init_solutions.pkl")
pickle.dump(dictionary, open(ss_init_path, "wb"))
if (chi_params_init <= 0.0).any():
# In case the minimizer doesn't respect the bounds given
output += 1e14
# Use generalized method of moments to fit the chi's
weighting_mat = np.eye(2 * J + S)
scaling_val = 100.0
value = np.dot(scaling_val * np.dot(output.reshape(1, 2 * J + S),
weighting_mat), scaling_val * output.reshape(2 * J + S, 1))
print 'Value of criterion function: ', value.sum()
# pickle output in case not converge
global Nfeval, value_all, chi_params_all
value_all[Nfeval] = value.sum()
chi_params_all[:,Nfeval] = chi_params_init
dict_GMM = dict([('values', value_all), ('chi_params', chi_params_all)])
ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_all.pkl")
pickle.dump(dict_GMM, open(ss_init_path, "wb"))
Nfeval += 1
return value.sum()
def SS_solver(b_guess_init, n_guess_init, wguess, rguess, T_Hguess,
factorguess, chi_n, chi_b, tax_params, params, iterative_params, tau_bq,
rho, lambdas, weights, e, fsolve_flag=False):
'''
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using an iterative method similar to TPI.
Inputs:
b_guess_init = guesses for b (SxJ array)
n_guess_init = guesses for n (SxJ array)
wguess = guess for wage rate (scalar)
rguess = guess for rental rate (scalar)
T_Hguess = guess for lump sum tax (scalar)
factorguess = guess for scaling factor to dollars (scalar)
chi_n = chi^n_s (Sx1 array)
chi_b = chi^b_j (Jx1 array)
params = list of parameters (list)
iterative_params = list of parameters that determine the convergence
of the while loop (list)
tau_bq = bequest tax rate (Jx1 array)
rho = mortality rates (Sx1 array)
lambdas = ability weights (Jx1 array)
weights = population weights (Sx1 array)
e = ability levels (SxJ array)
Outputs:
solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
'''
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 = params
analytical_mtrs, etr_params, mtrx_params, mtry_params = tax_params
maxiter, mindist_SS = iterative_params
# Rename the inputs
w = wguess
r = rguess
T_H = T_Hguess
factor = factorguess
bssmat = b_guess_init
nssmat = n_guess_init
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, T_H and
# factor
for j in xrange(J):
# Solve the euler equations
if j == 0:
guesses = np.append(bssmat[:, j], nssmat[:, j])
else:
guesses = np.append(bssmat[:, j-1], nssmat[:, j-1])
args_ = (r, w, T_H, factor, j, tax_params, params, chi_b, chi_n, tau_bq, rho,
lambdas, weights, e)
[solutions, infodict, ier, message] = opt.fsolve(Euler_equation_solver, guesses * .9,
args=args_, xtol=1e-13, full_output=True)
print 'Max Euler errors: ', np.absolute(infodict['fvec']).max()
bssmat[:, j] = solutions[:S]
nssmat[:, j] = solutions[S:]
# print np.array(Euler_equation_solver(np.append(bssmat[:, j],
# nssmat[:, j]), r, w, T_H, factor, j, params, chi_b, chi_n,
# theta, tau_bq, rho, lambdas, e)).max()
K = household.get_K(bssmat, weights.reshape(S, 1),
lambdas.reshape(1, J), g_n_ss, 'SS')
L = firm.get_L(e, nssmat, weights.reshape(S, 1),
lambdas.reshape(1, J), 'SS')
Y = firm.get_Y(K, L, params)
new_r = firm.get_r(Y, K, params)
new_w = firm.get_w(Y, L, params)
b_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat[:-1, :]))
average_income_model = ((new_r * b_s + new_w * e * nssmat) *
weights.reshape(S, 1) *
lambdas.reshape(1, J)).sum()
new_factor = mean_income_data / average_income_model
new_BQ = household.get_BQ(new_r, bssmat, weights.reshape(S, 1),
lambdas.reshape(1, J), rho.reshape(S, 1),
g_n_ss, 'SS')
theta = tax.replacement_rate_vals(nssmat, new_w, new_factor, e, J,
weights.reshape(S, 1), lambdas)
# lump_sum_tax_params = (a_etr_income, b_etr_income, c_etr_income, d_etr_income,
# e_etr_income, f_etr_income, min_x_etr_income, max_x_etr_income,
# min_y_etr_income, max_y_etr_income)
new_T_H = tax.get_lump_sum(new_r, b_s, new_w, e, nssmat, new_BQ,
lambdas.reshape(1, J), factor,
weights.reshape(S, 1), 'SS', etr_params, params, theta,
tau_bq)
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
T_H = utils.convex_combo(new_T_H, T_H, nu)
if T_H != 0:
dist = np.array([utils.perc_dif_func(new_r, r)] +
[utils.perc_dif_func(new_w, w)] +
[utils.perc_dif_func(new_T_H, T_H)] +
[utils.perc_dif_func(new_factor, factor)]).max()
else:
# If T_H is zero (if there are no taxes), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array([utils.perc_dif_func(new_r, r)] +
[utils.perc_dif_func(new_w, w)] +
[abs(new_T_H - T_H)] +
[utils.perc_dif_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
print 'New value of nu:', nu
iteration += 1
print "Iteration: %02d" % iteration, " Distance: ", dist
eul_errors = np.ones(J)
b_mat = np.zeros((S, J))
n_mat = np.zeros((S, J))
# Given the final w, r, T_H and factor, solve for the SS b and n (if you
# don't do a final fsolve, there will be a slight mismatch,
# with high euler errors)
for j in xrange(J):
guesses = np.append(bssmat[:, j], nssmat[:, j])
args_ = (r, w, T_H, factor, j, tax_params, params, chi_b, chi_n, tau_bq, rho,
lambdas, weights, e)
[solutions1, infodict, ier, message] = opt.fsolve(Euler_equation_solver, guesses * .9,
args=args_, xtol=1e-13, full_output=True)
eul_errors[j] = np.array(infodict['fvec']).max()
print 'Max Euler errors: ', np.absolute(infodict['fvec']).max()
b_mat[:, j] = solutions1[:S]
n_mat[:, j] = solutions1[S:]
print 'SS fsolve euler error:', eul_errors.max()
solutions = np.append(b_mat.flatten(), n_mat.flatten())
other_vars = np.array([w, r, factor, T_H])
solutions = np.append(solutions, other_vars)
return solutions
def function_to_minimize(chi_params_scalars, chi_params_init, params, iterative_params, weights_SS, rho_vec, lambdas, tau_bq, e):
'''
Inputs:
chi_params_scalars = guesses for multipliers for chi parameters ((S+J)x1 array)
chi_params_init = chi parameters that will be multiplied ((S+J)x1 array)
params = list of parameters (list)
weights_SS = steady state population weights (Sx1 array)
rho_vec = mortality rates (Sx1 array)
lambdas = ability weights (Jx1 array)
tau_bq = bequest tax rates (Jx1 array)
e = ability levels (Jx1 array)
Output:
The sum of absolute percent deviations between the actual and simulated wealth moments
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \
a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
chi_params_init *= chi_params_scalars
# print 'Print Chi_b: ', chi_params_init[:J]
# print 'Scaling vals:', chi_params_scalars[:J]
solutions_dict = pickle.load(open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S*J]
n_guess = solutions[S*J:2*S*J]
wguess, rguess, factorguess, T_Hguess = solutions[2*S*J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params_init[J:], chi_params_init[:J], params, iterative_params, tau_bq, rho, lambdas, weights_SS, e)
b_new = solutions[:S*J]
n_new = solutions[S*J:2*S*J]
w_new, r_new, factor_new, T_H_new = solutions[2*S*J:]
# Wealth Calibration Euler
error5 = list(utils.check_wealth_calibration(b_new.reshape(S, J)[:-1, :], factor_new, params))
# labor calibration euler
lab_data_dict = pickle.load(open("OUTPUT/Saved_moments/labor_data_moments.pkl", "rb"))
labor_sim = (n_new.reshape(S, J)*lambdas.reshape(1, J)).sum(axis=1)
if DATASET == 'SMALL':
lab_dist_data = lab_data_dict['labor_dist_data'][:S]
else:
lab_dist_data = lab_data_dict['labor_dist_data']
error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data))
# combine eulers
output = np.array(error5 + error6)
# Constraints
eul_error = np.ones(J)
for j in xrange(J):
eul_error[j] = np.abs(Euler_equation_solver(np.append(b_new.reshape(S, J)[:, j], n_new.reshape(S, J)[:, j]), r_new, w_new, T_H_new, factor_new, j, params, chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas, weights_SS, e)).max()
fsolve_no_converg = eul_error.max()
if np.isnan(fsolve_no_converg):
fsolve_no_converg = 1e6
if fsolve_no_converg > 1e-4:
# If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be
# and don't save the solutions as initial guesses (since they might be gibberish)
output += 1e14
else:
var_names = ['solutions']
dictionary = {}
for key in var_names:
dictionary[key] = locals()[key]
pickle.dump(dictionary, open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "wb"))
if (chi_params_init <= 0.0).any():
# In case the minimizer doesn't respect the bounds given
output += 1e14
# Use generalized method of moments to fit the chi's
weighting_mat = np.eye(2*J + S)
scaling_val = 100.0
value = np.dot(scaling_val * np.dot(output.reshape(1, 2*J+S), weighting_mat), scaling_val * output.reshape(2*J+S, 1))
print 'Value of criterion function: ', value.sum()
return value.sum()
def function_to_minimize(chi_params_scalars, chi_params_init,
income_tax_parameters, ss_parameters,
iterative_params, weights_SS, rho_vec, lambdas,
tau_bq, e, output_dir):
'''
Inputs:
chi_params_scalars = guesses for multipliers for chi parameters
((S+J)x1 array)
chi_params_init = chi parameters that will be multiplied
((S+J)x1 array)
params = list of parameters (list)
weights_SS = steady state population weights (Sx1 array)
rho_vec = mortality rates (Sx1 array)
lambdas = ability weights (Jx1 array)
tau_bq = bequest tax rates (Jx1 array)
e = ability levels (Jx1 array)
Output:
The sum of absolute percent deviations between the actual and
simulated wealth moments
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters
chi_params_init *= chi_params_scalars
# print 'Print Chi_b: ', chi_params_init[:J]
# print 'Scaling vals:', chi_params_scalars[:J]
ss_init_path = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:(S * J)]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[(2 * S * J):]
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params_init[J:],
chi_params_init[:J], income_tax_parameters, ss_parameters,
iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve,
guesses,
args=args_,
xtol=mindist_SS,
full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess,
rguess, T_Hguess, factorguess, chi_params_init[J:],
chi_params_init[:J], income_tax_parameters,
ss_parameters, iterative_params, tau_bq, rho,
lambdas, omega_SS, e, fsolve_flag)
b_new = solutions[:(S * J)]
n_new = solutions[(S * J):(2 * S * J)]
w_new, r_new, factor_new, T_H_new = solutions[(2 * S * J):]
# Wealth Calibration Euler
error5 = list(
utils.check_wealth_calibration(
b_new.reshape(S, J)[:-1, :], factor_new, ss_parameters,
output_dir))
# labor calibration euler
labor_path = os.path.join(output_dir,
"Saved_moments/labor_data_moments.pkl")
lab_data_dict = pickle.load(open(labor_path, "rb"))
labor_sim = (n_new.reshape(S, J) * lambdas.reshape(1, J)).sum(axis=1)
if DATASET == 'SMALL':
lab_dist_data = lab_data_dict['labor_dist_data'][:S]
else:
lab_dist_data = lab_data_dict['labor_dist_data']
error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data))
# combine eulers
output = np.array(error5 + error6)
# Constraints
eul_error = np.ones(J)
for j in xrange(J):
eul_error[j] = np.abs(
Euler_equation_solver(
np.append(
b_new.reshape(S, J)[:, j],
n_new.reshape(S, J)[:, j]), r_new, w_new, T_H_new,
factor_new, j, income_tax_parameters, ss_parameters,
chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas,
weights_SS, e)).max()
fsolve_no_converg = eul_error.max()
if np.isnan(fsolve_no_converg):
fsolve_no_converg = 1e6
if fsolve_no_converg > 1e-4:
# If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be
# and don't save the solutions as initial guesses (since they might be
# gibberish)
output += 1e14
else:
var_names = ['solutions']
dictionary = {}
for key in var_names:
dictionary[key] = locals()[key]
ss_init_path = os.path.join(output_dir,
"Saved_moments/SS_init_solutions.pkl")
pickle.dump(dictionary, open(ss_init_path, "wb"))
if (chi_params_init <= 0.0).any():
# In case the minimizer doesn't respect the bounds given
output += 1e14
# Use generalized method of moments to fit the chi's
weighting_mat = np.eye(2 * J + S)
scaling_val = 100.0
value = np.dot(
scaling_val * np.dot(output.reshape(1, 2 * J + S), weighting_mat),
scaling_val * output.reshape(2 * J + S, 1))
print 'Value of criterion function: ', value.sum()
# pickle output in case not converge
global Nfeval, value_all, chi_params_all
value_all[Nfeval] = value.sum()
chi_params_all[:, Nfeval] = chi_params_init
dict_GMM = dict([('values', value_all), ('chi_params', chi_params_all)])
ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_all.pkl")
pickle.dump(dict_GMM, open(ss_init_path, "wb"))
Nfeval += 1
return value.sum()
def SS_solver(b_guess_init,
n_guess_init,
wguess,
rguess,
T_Hguess,
factorguess,
chi_n,
chi_b,
tax_params,
params,
iterative_params,
tau_bq,
rho,
lambdas,
weights,
e,
fsolve_flag=False):
'''
Solves for the steady state distribution of capital, labor, as well as
w, r, T_H and the scaling factor, using an iterative method similar to TPI.
Inputs:
b_guess_init = guesses for b (SxJ array)
n_guess_init = guesses for n (SxJ array)
wguess = guess for wage rate (scalar)
rguess = guess for rental rate (scalar)
T_Hguess = guess for lump sum tax (scalar)
factorguess = guess for scaling factor to dollars (scalar)
chi_n = chi^n_s (Sx1 array)
chi_b = chi^b_j (Jx1 array)
params = list of parameters (list)
iterative_params = list of parameters that determine the convergence
of the while loop (list)
tau_bq = bequest tax rate (Jx1 array)
rho = mortality rates (Sx1 array)
lambdas = ability weights (Jx1 array)
weights = population weights (Sx1 array)
e = ability levels (SxJ array)
Outputs:
solutions = steady state values of b, n, w, r, factor,
T_H ((2*S*J+4)x1 array)
'''
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 = params
analytical_mtrs, etr_params, mtrx_params, mtry_params = tax_params
maxiter, mindist_SS = iterative_params
# Rename the inputs
w = wguess
r = rguess
T_H = T_Hguess
factor = factorguess
bssmat = b_guess_init
nssmat = n_guess_init
dist = 10
iteration = 0
dist_vec = np.zeros(maxiter)
if fsolve_flag == True:
maxiter = 1
while (dist > mindist_SS) and (iteration < maxiter):
# Solve for the steady state levels of b and n, given w, r, T_H and
# factor
for j in xrange(J):
# Solve the euler equations
if j == 0:
guesses = np.append(bssmat[:, j], nssmat[:, j])
else:
guesses = np.append(bssmat[:, j - 1], nssmat[:, j - 1])
args_ = (r, w, T_H, factor, j, tax_params, params, chi_b, chi_n,
tau_bq, rho, lambdas, weights, e)
[solutions, infodict, ier,
message] = opt.fsolve(Euler_equation_solver,
guesses * .9,
args=args_,
xtol=1e-13,
full_output=True)
print 'Max Euler errors: ', np.absolute(infodict['fvec']).max()
bssmat[:, j] = solutions[:S]
nssmat[:, j] = solutions[S:]
# print np.array(Euler_equation_solver(np.append(bssmat[:, j],
# nssmat[:, j]), r, w, T_H, factor, j, params, chi_b, chi_n,
# theta, tau_bq, rho, lambdas, e)).max()
K = household.get_K(bssmat, weights.reshape(S, 1),
lambdas.reshape(1, J), g_n_ss, 'SS')
L = firm.get_L(e, nssmat, weights.reshape(S, 1), lambdas.reshape(1, J),
'SS')
Y = firm.get_Y(K, L, params)
new_r = firm.get_r(Y, K, params)
new_w = firm.get_w(Y, L, params)
b_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat[:-1, :]))
average_income_model = ((new_r * b_s + new_w * e * nssmat) *
weights.reshape(S, 1) *
lambdas.reshape(1, J)).sum()
new_factor = mean_income_data / average_income_model
new_BQ = household.get_BQ(new_r, bssmat, weights.reshape(S, 1),
lambdas.reshape(1, J), rho.reshape(S, 1),
g_n_ss, 'SS')
theta = tax.replacement_rate_vals(nssmat, new_w, new_factor, e, J,
weights.reshape(S, 1), lambdas)
# lump_sum_tax_params = (a_etr_income, b_etr_income, c_etr_income, d_etr_income,
# e_etr_income, f_etr_income, min_x_etr_income, max_x_etr_income,
# min_y_etr_income, max_y_etr_income)
new_T_H = tax.get_lump_sum(new_r, b_s, new_w, e, nssmat, new_BQ,
lambdas.reshape(1, J), factor,
weights.reshape(S, 1), 'SS', etr_params,
params, theta, tau_bq)
r = utils.convex_combo(new_r, r, nu)
w = utils.convex_combo(new_w, w, nu)
factor = utils.convex_combo(new_factor, factor, nu)
T_H = utils.convex_combo(new_T_H, T_H, nu)
if T_H != 0:
dist = np.array([utils.perc_dif_func(new_r, r)] +
[utils.perc_dif_func(new_w, w)] +
[utils.perc_dif_func(new_T_H, T_H)] +
[utils.perc_dif_func(new_factor, factor)]).max()
else:
# If T_H is zero (if there are no taxes), a percent difference
# will throw NaN's, so we use an absoluate difference
dist = np.array([utils.perc_dif_func(new_r, r)] +
[utils.perc_dif_func(new_w, w)] +
[abs(new_T_H - T_H)] +
[utils.perc_dif_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu /= 2.0
print 'New value of nu:', nu
iteration += 1
print "Iteration: %02d" % iteration, " Distance: ", dist
eul_errors = np.ones(J)
b_mat = np.zeros((S, J))
n_mat = np.zeros((S, J))
# Given the final w, r, T_H and factor, solve for the SS b and n (if you
# don't do a final fsolve, there will be a slight mismatch,
# with high euler errors)
for j in xrange(J):
guesses = np.append(bssmat[:, j], nssmat[:, j])
args_ = (r, w, T_H, factor, j, tax_params, params, chi_b, chi_n,
tau_bq, rho, lambdas, weights, e)
[solutions1, infodict, ier,
message] = opt.fsolve(Euler_equation_solver,
guesses * .9,
args=args_,
xtol=1e-13,
full_output=True)
eul_errors[j] = np.array(infodict['fvec']).max()
print 'Max Euler errors: ', np.absolute(infodict['fvec']).max()
b_mat[:, j] = solutions1[:S]
n_mat[:, j] = solutions1[S:]
print 'SS fsolve euler error:', eul_errors.max()
solutions = np.append(b_mat.flatten(), n_mat.flatten())
other_vars = np.array([w, r, factor, T_H])
solutions = np.append(solutions, other_vars)
return solutions