def get_tax_function_parameters(self, client, run_micro=False):
# Income tax parameters
if self.baseline:
tx_func_est_path = os.path.join(
self.output_base, 'TxFuncEst_baseline{}.pkl'.format(self.guid),
)
else:
tx_func_est_path = os.path.join(
self.output_base, 'TxFuncEst_policy{}.pkl'.format(self.guid),
)
if run_micro:
txfunc.get_tax_func_estimate(
self.BW, self.S, self.starting_age, self.ending_age,
self.baseline, self.analytical_mtrs, self.tax_func_type,
self.age_specific, self.start_year, self.reform, self.guid,
tx_func_est_path, self.data, client, self.num_workers)
if self.baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(self.guid)
estimate_file = tx_func_est_path
print('Using baseline tax parameters from ', tx_func_est_path)
dict_params = self.read_tax_func_estimate(estimate_file,
baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(self.guid)
estimate_file = tx_func_est_path
print('Using reform policy tax parameters from ', tx_func_est_path)
dict_params = self.read_tax_func_estimate(estimate_file,
policy_pckl)
self.mean_income_data = dict_params['tfunc_avginc'][0]
try:
self.taxcalc_version = dict_params['taxcalc_version']
except KeyError:
self.taxcalc_version = 'No version recorded'
# Reorder indices of tax function and tile for all years after
# budget window ends
num_etr_params = dict_params['tfunc_etr_params_S'].shape[2]
num_mtrx_params = dict_params['tfunc_mtrx_params_S'].shape[2]
num_mtry_params = dict_params['tfunc_mtry_params_S'].shape[2]
# First check to see if tax parameters that are used were
# estimated with a budget window and ages that are as long as
# the those implied based on the start year and model age.
# N.B. the tax parameters dictionary does not save the years
# that correspond to the parameter estimates, so the start year
# used there may name match what is used in a run that reads in
# some cached tax function parameters. Likewise for age.
params_list = ['etr', 'mtrx', 'mtry']
BW_in_tax_params = dict_params['tfunc_etr_params_S'].shape[1]
S_in_tax_params = dict_params['tfunc_etr_params_S'].shape[0]
if self.BW != BW_in_tax_params:
print('Warning: There is a discrepency between the start' +
' year of the model and that of the tax functions!!')
# After printing warning, make it work by tiling
if self.BW > BW_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][:, -1, :].
reshape(S_in_tax_params, 1, num_etr_params),
(1, self.BW - BW_in_tax_params, 1))),
axis=1)
dict_params['tfunc_avg_' + item] =\
np.append(dict_params['tfunc_avg_' + item],
np.tile(dict_params['tfunc_avg_' + item][-1],
(self.BW - BW_in_tax_params)))
if self.S != S_in_tax_params:
print('Warning: There is a discrepency between the ages' +
' used in the model and those in the tax functions!!')
# After printing warning, make it work by tiling
if self.S > S_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][-1, :, :].
reshape(1, self.BW, num_etr_params),
(self.S - S_in_tax_params, 1, 1))),
axis=0)
self.etr_params = np.empty((self.T, self.S, num_etr_params))
self.mtrx_params = np.empty((self.T, self.S, num_mtrx_params))
self.mtry_params = np.empty((self.T, self.S, num_mtry_params))
self.etr_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.etr_params[self.BW:, :, :] =\
np.tile(np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_etr_params), axes=[1, 0, 2]),
(self.T - self.BW, 1, 1))
self.mtrx_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtrx_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtrx_params), axes=[1, 0, 2])
self.mtry_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtry_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtry_params), axes=[1, 0, 2])
if self.constant_rates:
print('Using constant rates!')
# # Make all ETRs equal the average
self.etr_params = np.zeros(self.etr_params.shape)
# set shift to average rate
self.etr_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_etr'].reshape(self.BW, 1),
(1, self.S))
self.etr_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_etr'][-1]
# # Make all MTRx equal the average
self.mtrx_params = np.zeros(self.mtrx_params.shape)
# set shift to average rate
self.mtrx_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtrx'].reshape(self.BW, 1),
(1, self.S))
self.mtrx_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtrx'][-1]
# # Make all MTRy equal the average
self.mtry_params = np.zeros(self.mtry_params.shape)
# set shift to average rate
self.mtry_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtry'].reshape(self.BW, 1),
(1, self.S))
self.mtry_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtry'][-1]
if self.zero_taxes:
print('Zero taxes!')
self.etr_params = np.zeros(self.etr_params.shape)
self.mtrx_params = np.zeros(self.mtrx_params.shape)
self.mtry_params = np.zeros(self.mtry_params.shape)
def runner(output_base,
input_dir,
baseline=False,
analytical_mtrs=True,
reform={},
user_params={},
guid='',
run_micro=True):
from ogusa import parameters, wealth, labor, demographics, income
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
reform=reform,
guid=guid)
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
globals().update(run_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir=input_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir=input_dir)
get_baseline = True
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'analytical_mtrs', 'b_ellipse',
'k_ellipse', 'upsilon', 'chi_b_guess', 'chi_n_guess', 'etr_params',
'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq',
'calibrate_model', 'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth', 'get_baseline', 'omega', 'g_n_ss',
'omega_SS', 'surv_rate', 'e', 'rho'
]
'''
------------------------------------------------------------------------
Run SS with minimization to fit chi_b and chi_n
------------------------------------------------------------------------
'''
# This is the simulation before getting the replacement rate values
sim_params = {}
glbs = globals()
lcls = locals()
for key in param_names:
if key in glbs:
sim_params[key] = glbs[key]
else:
sim_params[key] = lcls[key]
sim_params['output_dir'] = input_dir
sim_params['run_params'] = run_params
income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_steady_state(income_tax_params,
ss_parameters,
iterative_params,
get_baseline,
calibrate_model,
output_dir=input_dir)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
sim_params['input_dir'] = input_dir
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 = TPI.create_tpi_params(**sim_params)
ss_outputs['income_tax_params'] = income_tax_params
ss_outputs['wealth_tax_params'] = wealth_tax_params
ss_outputs['ellipse_params'] = ellipse_params
ss_outputs['parameters'] = parameters
ss_outputs['N_tilde'] = N_tilde
ss_outputs['omega_stationary'] = omega_stationary
ss_outputs['K0'] = K0
ss_outputs['b_sinit'] = b_sinit
ss_outputs['b_splus1init'] = b_splus1init
ss_outputs['L0'] = L0
ss_outputs['Y0'] = Y0
ss_outputs['r0'] = r0
ss_outputs['BQ0'] = BQ0
ss_outputs['T_H_0'] = T_H_0
ss_outputs['tax0'] = tax0
ss_outputs['c0'] = c0
ss_outputs['initial_b'] = initial_b
ss_outputs['initial_n'] = initial_n
ss_outputs['tau_bq'] = tau_bq
ss_outputs['g_n_vector'] = g_n_vector
ss_outputs['output_dir'] = input_dir
with open("ss_outputs.pkl", 'wb') as fp:
pickle.dump(ss_outputs, fp)
w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_time_path_iteration(
**ss_outputs)
print "getting to here...."
TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - tick)
def runner(output_base,
baseline_dir,
test=False,
time_path=True,
baseline=False,
analytical_mtrs=False,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True,
small_open=False,
budget_balance=False,
baseline_spending=False):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
# Make sure options are internally consistent
if baseline == True and baseline_spending == True:
print(
'Inconsistent options. Setting <baseline_spending> to False, leaving <baseline> True.'
)
baseline_spending = False
if budget_balance == True and baseline_spending == True:
print(
'Inconsistent options. Setting <baseline_spending> to False, leaving <budget_balance> True.'
)
baseline_spending = False
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params['start_year'],
reform=reform,
guid=guid)
print('In runner, baseline is ', baseline)
run_params = ogusa.parameters.get_parameters(test=test,
baseline=baseline,
guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
run_params['small_open'] = small_open
run_params['budget_balance'] = budget_balance
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print("updating frisch and associated")
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
if 'debt_ratio_ss' in user_params:
run_params['debt_ratio_ss'] = user_params['debt_ratio_ss']
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print("updating g_y_annual and associated")
ending_age = run_params['ending_age']
starting_age = run_params['starting_age']
S = run_params['S']
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
# Modify transfer & spending ratios based on user input.
if 'T_shifts' in user_params:
if baseline_spending == False:
print('updating ALPHA_T with T_shifts in first',
user_params['T_shifts'].size, 'periods.')
T_shifts = np.concatenate((user_params['T_shifts'],
np.zeros(run_params['ALPHA_T'].size -
user_params['T_shifts'].size)),
axis=0)
run_params['ALPHA_T'] = run_params['ALPHA_T'] + T_shifts
if 'G_shifts' in user_params:
if baseline_spending == False:
print('updating ALPHA_G with G_shifts in first',
user_params['G_shifts'].size, 'periods.')
G_shifts = np.concatenate((user_params['G_shifts'],
np.zeros(run_params['ALPHA_G'].size -
user_params['G_shifts'].size)),
axis=0)
run_params['ALPHA_G'] = run_params['ALPHA_G'] + G_shifts
from ogusa import SS, TPI
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'gamma', 'epsilon', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon', 'small_open',
'budget_balance', 'ss_firm_r', 'ss_hh_r', 'tpi_firm_r', 'tpi_hh_r',
'tG1', 'tG2', 'alpha_T', 'alpha_G', 'ALPHA_T', 'ALPHA_G', 'rho_G',
'debt_ratio_ss', 'tau_b', 'delta_tau', 'chi_b_guess', 'chi_n_guess',
'etr_params', 'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth',
'm_wealth', 'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'imm_rates',
'e', 'rho', 'initial_debt', 'omega_S_preTP'
]
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params, small_open_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_SS(income_tax_params,
ss_parameters,
iterative_params,
chi_params,
small_open_params,
baseline,
baseline_spending,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
sim_params['baseline'] = baseline
sim_params['baseline_spending'] = baseline_spending
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, small_open_params, initial_values, SS_values, fiscal_params, biz_tax_params = TPI.create_tpi_params(
**sim_params)
tpi_output, macro_output = TPI.run_TPI(
income_tax_params,
tpi_params,
iterative_params,
small_open_params,
initial_values,
SS_values,
fiscal_params,
biz_tax_params,
output_dir=output_base,
baseline_spending=baseline_spending)
'''
------------------------------------------------------------------------
Pickle TPI results
------------------------------------------------------------------------
'''
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(tpi_output, open(tpi_vars, "wb"))
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_macro_vars.pkl")
pickle.dump(macro_output, open(tpi_vars, "wb"))
print("Time path iteration complete.")
print("It took {0} seconds to get that part done.".format(time.time() -
tick))
def runner_SS(output_base, baseline_dir, baseline=False,
analytical_mtrs=False, age_specific=False, reform={}, user_params={},
guid='', run_micro=True):
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific,
start_year=user_params['start_year'], reform=reform, guid=guid)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
ending_age = run_params['ending_age']
starting_age = run_params['starting_age']
S = run_params['S']
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'imm_rates', 'e', 'rho', 'omega_S_preTP']
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_params, iterative_params, chi_params= SS.create_steady_state_parameters(**sim_params)
'''
****
CALL CALIBRATION here if boolean flagged
****
'''
calibrate_model = False
if calibrate_model:
chi_params = calibrate.chi_estimate(income_tax_params, ss_params, iterative_params, chi_params, baseline_dir=baseline_dir)
ss_outputs = SS.run_SS(income_tax_params, ss_params, iterative_params, chi_params, baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
def runner(output_base, baseline_dir, baseline=False, analytical_mtrs=True, age_specific=False, reform={}, user_params={}, guid='', run_micro=True):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, wealth, labor, demog, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific,
start_year=user_params['start_year'], reform=reform, guid=guid)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(run_params['lambdas'], run_params['J'], run_params['flag_graphs'], output_dir=output_base)
# Generate labor data moments
labor.labor_data_moments(run_params['flag_graphs'], output_dir=output_base)
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho']
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_SS(income_tax_params, ss_parameters, iterative_params, chi_params, baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_dir, "SS"))
ss_dir = os.path.join(output_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, initial_values, SS_values = TPI.create_tpi_params(**sim_params)
# ss_outputs['income_tax_params'] = income_tax_params
# ss_outputs['wealth_tax_params'] = wealth_tax_params
# ss_outputs['ellipse_params'] = ellipse_params
# ss_outputs['parameters'] = parameters
# ss_outputs['N_tilde'] = N_tilde
# ss_outputs['omega_stationary'] = omega_stationary
# ss_outputs['K0'] = K0
# ss_outputs['b_sinit'] = b_sinit
# ss_outputs['b_splus1init'] = b_splus1init
# ss_outputs['L0'] = L0
# ss_outputs['Y0'] = Y0
# ss_outputs['r0'] = r0
# ss_outputs['BQ0'] = BQ0
# ss_outputs['T_H_0'] = T_H_0
# ss_outputs['factor_ss'] = factor
# ss_outputs['tax0'] = tax0
# ss_outputs['c0'] = c0
# ss_outputs['initial_b'] = initial_b
# ss_outputs['initial_n'] = initial_n
# ss_outputs['tau_bq'] = tau_bq
# ss_outputs['g_n_vector'] = g_n_vector
# ss_outputs['output_dir'] = output_base
# with open("ss_outputs.pkl", 'wb') as fp:
# pickle.dump(ss_outputs, fp)
w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_TPI(income_tax_params,
tpi_params, iterative_params, initial_values, SS_values, output_dir=output_base)
print "getting to here...."
TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - tick)
def get_tax_function_parameters(self,
client,
run_micro=False,
tax_func_path=None):
'''
Reads pickle file of tax function parameters or estimates the
parameters from microsimulation model output.
Args:
client (Dask client object): client
run_micro (bool): whether to estimate parameters from
microsimulation model
tax_func_path (string): path where find or save tax
function parameter estimates
Returns:
None
'''
# set paths if none given
if tax_func_path is None:
if self.baseline:
pckl = "TxFuncEst_baseline{}.pkl".format(self.guid)
tax_func_path = os.path.join(self.output_base, pckl)
print('Using baseline tax parameters from ', tax_func_path)
else:
pckl = "TxFuncEst_policy{}.pkl".format(self.guid)
tax_func_path = os.path.join(self.output_base, pckl)
print('Using reform policy tax parameters from ',
tax_func_path)
# If run_micro is false, check to see if parameters file exists
# and if it is consistent with Specifications instance
if not run_micro:
dict_params, run_micro = self.read_tax_func_estimate(tax_func_path)
if run_micro:
txfunc.get_tax_func_estimate( # pragma: no cover
self.BW, self.S, self.starting_age, self.ending_age,
self.baseline, self.analytical_mtrs, self.tax_func_type,
self.age_specific, self.start_year, self.iit_reform, self.guid,
tax_func_path, self.data, client, self.num_workers)
dict_params, _ = self.read_tax_func_estimate(tax_func_path)
self.mean_income_data = dict_params['tfunc_avginc'][0]
try:
self.frac_tax_payroll = np.append(
dict_params['tfunc_frac_tax_payroll'],
np.ones(self.T + self.S - self.BW) *
dict_params['tfunc_frac_tax_payroll'][-1])
except KeyError:
pass
try:
self.taxcalc_version = dict_params['taxcalc_version']
except KeyError:
self.taxcalc_version = 'No version recorded'
# Reorder indices of tax function and tile for all years after
# budget window ends
num_etr_params = dict_params['tfunc_etr_params_S'].shape[2]
num_mtrx_params = dict_params['tfunc_mtrx_params_S'].shape[2]
num_mtry_params = dict_params['tfunc_mtry_params_S'].shape[2]
# First check to see if tax parameters that are used were
# estimated with a budget window and ages that are as long as
# the those implied based on the start year and model age.
# N.B. the tax parameters dictionary does not save the years
# that correspond to the parameter estimates, so the start year
# used there may name match what is used in a run that reads in
# some cached tax function parameters. Likewise for age.
params_list = ['etr', 'mtrx', 'mtry']
BW_in_tax_params = dict_params['tfunc_etr_params_S'].shape[1]
S_in_tax_params = dict_params['tfunc_etr_params_S'].shape[0]
if self.BW != BW_in_tax_params:
print('Warning: There is a discrepency between the start' +
' year of the model and that of the tax functions!!')
# After printing warning, make it work by tiling
if self.BW > BW_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][:, -1, :].
reshape(S_in_tax_params, 1, num_etr_params),
(1, self.BW - BW_in_tax_params, 1))),
axis=1)
dict_params['tfunc_avg_' + item] =\
np.append(dict_params['tfunc_avg_' + item],
np.tile(dict_params['tfunc_avg_' + item][-1],
(self.BW - BW_in_tax_params)))
if self.S != S_in_tax_params:
print('Warning: There is a discrepency between the ages' +
' used in the model and those in the tax functions!!')
# After printing warning, make it work by tiling
if self.S > S_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][-1, :, :].
reshape(1, self.BW, num_etr_params),
(self.S - S_in_tax_params, 1, 1))),
axis=0)
self.etr_params = np.empty((self.T, self.S, num_etr_params))
self.mtrx_params = np.empty((self.T, self.S, num_mtrx_params))
self.mtry_params = np.empty((self.T, self.S, num_mtry_params))
self.etr_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.etr_params[self.BW:, :, :] =\
np.tile(np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_etr_params), axes=[1, 0, 2]),
(self.T - self.BW, 1, 1))
self.mtrx_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtrx_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtrx_params), axes=[1, 0, 2])
self.mtry_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtry_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtry_params), axes=[1, 0, 2])
if self.constant_rates:
print('Using constant rates!')
# # Make all ETRs equal the average
self.etr_params = np.zeros(self.etr_params.shape)
# set shift to average rate
self.etr_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_etr'].reshape(self.BW, 1), (1, self.S))
self.etr_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_etr'][-1]
# # Make all MTRx equal the average
self.mtrx_params = np.zeros(self.mtrx_params.shape)
# set shift to average rate
self.mtrx_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtrx'].reshape(self.BW, 1), (1, self.S))
self.mtrx_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtrx'][-1]
# # Make all MTRy equal the average
self.mtry_params = np.zeros(self.mtry_params.shape)
# set shift to average rate
self.mtry_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtry'].reshape(self.BW, 1), (1, self.S))
self.mtry_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtry'][-1]
if self.zero_taxes:
print('Zero taxes!')
self.etr_params = np.zeros(self.etr_params.shape)
self.mtrx_params = np.zeros(self.mtrx_params.shape)
self.mtry_params = np.zeros(self.mtry_params.shape)
def runner_SS(output_base,
baseline_dir,
baseline=False,
analytical_mtrs=True,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True):
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params['start_year'],
reform=reform,
guid=guid)
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
'''
****
CALL CALIBRATION here if boolean flagged
****
'''
calibrate_model = False
# if calibrate_model:
# chi_b, chi_n = calibrate.(income_tax_params, ss_params, iterative_params, chi_params, baseline,
# calibrate_model, output_dir=output_base, baseline_dir=baseline_dir)
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'analytical_mtrs', 'b_ellipse',
'k_ellipse', 'upsilon', 'chi_b_guess', 'chi_n_guess', 'etr_params',
'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq', 'retire',
'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho'
]
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_params, iterative_params, chi_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_SS(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
def get_tax_function_parameters(self, client, run_micro=False):
# Income tax parameters
if self.baseline:
tx_func_est_path = os.path.join(
self.output_base, 'TxFuncEst_baseline{}.pkl'.format(self.guid),
)
else:
tx_func_est_path = os.path.join(
self.output_base, 'TxFuncEst_policy{}.pkl'.format(self.guid),
)
if run_micro:
txfunc.get_tax_func_estimate(
self.BW, self.S, self.starting_age, self.ending_age,
self.baseline, self.analytical_mtrs, self.tax_func_type,
self.age_specific, self.start_year, self.reform, self.guid,
tx_func_est_path, self.data, client, self.num_workers)
if self.baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(self.guid)
estimate_file = tx_func_est_path
print('Using baseline tax parameters from ', tx_func_est_path)
dict_params = self.read_tax_func_estimate(estimate_file,
baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(self.guid)
estimate_file = tx_func_est_path
print('Using reform policy tax parameters from ', tx_func_est_path)
dict_params = self.read_tax_func_estimate(estimate_file,
policy_pckl)
self.mean_income_data = dict_params['tfunc_avginc'][0]
try:
self.taxcalc_version = dict_params['taxcalc_version']
except KeyError:
self.taxcalc_version = 'No version recorded'
# Reorder indices of tax function and tile for all years after
# budget window ends
num_etr_params = dict_params['tfunc_etr_params_S'].shape[2]
num_mtrx_params = dict_params['tfunc_mtrx_params_S'].shape[2]
num_mtry_params = dict_params['tfunc_mtry_params_S'].shape[2]
# First check to see if tax parameters that are used were
# estimated with a budget window and ages that are as long as
# the those implied based on the start year and model age.
# N.B. the tax parameters dictionary does not save the years
# that correspond to the parameter estimates, so the start year
# used there may name match what is used in a run that reads in
# some cached tax function parameters. Likewise for age.
params_list = ['etr', 'mtrx', 'mtry']
BW_in_tax_params = dict_params['tfunc_etr_params_S'].shape[1]
S_in_tax_params = dict_params['tfunc_etr_params_S'].shape[0]
if self.BW != BW_in_tax_params:
print('Warning: There is a discrepency between the start' +
' year of the model and that of the tax functions!!')
# After printing warning, make it work by tiling
if self.BW > BW_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][:, -1, :].
reshape(S_in_tax_params, 1, num_etr_params),
(1, self.BW - BW_in_tax_params, 1))),
axis=1)
dict_params['tfunc_avg_' + item] =\
np.append(dict_params['tfunc_avg_' + item],
np.tile(dict_params['tfunc_avg_' + item][-1],
(self.BW - BW_in_tax_params)))
if self.S != S_in_tax_params:
print('Warning: There is a discrepency between the ages' +
' used in the model and those in the tax functions!!')
# After printing warning, make it work by tiling
if self.S > S_in_tax_params:
for item in params_list:
dict_params['tfunc_' + item + '_params_S'] =\
np.concatenate(
(dict_params['tfunc_' + item + '_params_S'],
np.tile(dict_params['tfunc_' + item +
'_params_S'][-1, :, :].
reshape(1, self.BW, num_etr_params),
(self.S - S_in_tax_params, 1, 1))),
axis=0)
self.etr_params = np.empty((self.T, self.S, num_etr_params))
self.mtrx_params = np.empty((self.T, self.S, num_mtrx_params))
self.mtry_params = np.empty((self.T, self.S, num_mtry_params))
self.etr_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.etr_params[self.BW:, :, :] =\
np.tile(np.transpose(
dict_params['tfunc_etr_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_etr_params), axes=[1, 0, 2]),
(self.T - self.BW, 1, 1))
self.mtrx_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtrx_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtrx_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtrx_params), axes=[1, 0, 2])
self.mtry_params[:self.BW, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, :self.BW, :],
axes=[1, 0, 2])
self.mtry_params[self.BW:, :, :] =\
np.transpose(
dict_params['tfunc_mtry_params_S'][:self.S, -1, :].reshape(
self.S, 1, num_mtry_params), axes=[1, 0, 2])
if self.constant_rates:
print('Using constant rates!')
# # Make all ETRs equal the average
self.etr_params = np.zeros(self.etr_params.shape)
# set shift to average rate
self.etr_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_etr'].reshape(self.BW, 1),
(1, self.S))
self.etr_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_etr'][-1]
# # Make all MTRx equal the average
self.mtrx_params = np.zeros(self.mtrx_params.shape)
# set shift to average rate
self.mtrx_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtrx'].reshape(self.BW, 1),
(1, self.S))
self.mtrx_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtrx'][-1]
# # Make all MTRy equal the average
self.mtry_params = np.zeros(self.mtry_params.shape)
# set shift to average rate
self.mtry_params[:self.BW, :, 10] = np.tile(
dict_params['tfunc_avg_mtry'].reshape(self.BW, 1),
(1, self.S))
self.mtry_params[self.BW:, :, 10] =\
dict_params['tfunc_avg_mtry'][-1]
if self.zero_taxes:
print('Zero taxes!')
self.etr_params = np.zeros(self.etr_params.shape)
self.mtrx_params = np.zeros(self.mtrx_params.shape)
self.mtry_params = np.zeros(self.mtry_params.shape)
def runner(output_base, input_dir, baseline=False, analytical_mtrs=True, age_specific=False, reform={}, user_params={}, guid='', run_micro=True):
from ogusa import parameters, wealth, labor, demographics, income
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific, reform=reform, guid=guid)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
globals().update(run_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir=input_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir=input_dir)
get_baseline = True
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq', 'calibrate_model',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth', 'get_baseline',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho']
'''
------------------------------------------------------------------------
Run SS with minimization to fit chi_b and chi_n
------------------------------------------------------------------------
'''
# This is the simulation before getting the replacement rate values
sim_params = {}
glbs = globals()
lcls = locals()
for key in param_names:
if key in glbs:
sim_params[key] = glbs[key]
else:
sim_params[key] = lcls[key]
sim_params['output_dir'] = input_dir
sim_params['run_params'] = run_params
income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_steady_state(income_tax_params, ss_parameters, iterative_params, get_baseline, calibrate_model, output_dir=input_dir)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
sim_params['input_dir'] = input_dir
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 = TPI.create_tpi_params(**sim_params)
ss_outputs['income_tax_params'] = income_tax_params
ss_outputs['wealth_tax_params'] = wealth_tax_params
ss_outputs['ellipse_params'] = ellipse_params
ss_outputs['parameters'] = parameters
ss_outputs['N_tilde'] = N_tilde
ss_outputs['omega_stationary'] = omega_stationary
ss_outputs['K0'] = K0
ss_outputs['b_sinit'] = b_sinit
ss_outputs['b_splus1init'] = b_splus1init
ss_outputs['L0'] = L0
ss_outputs['Y0'] = Y0
ss_outputs['r0'] = r0
ss_outputs['BQ0'] = BQ0
ss_outputs['T_H_0'] = T_H_0
ss_outputs['tax0'] = tax0
ss_outputs['c0'] = c0
ss_outputs['initial_b'] = initial_b
ss_outputs['initial_n'] = initial_n
ss_outputs['tau_bq'] = tau_bq
ss_outputs['g_n_vector'] = g_n_vector
ss_outputs['output_dir'] = input_dir
with open("ss_outputs.pkl", 'wb') as fp:
pickle.dump(ss_outputs, fp)
w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_time_path_iteration(**ss_outputs)
print "getting to here...."
TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - tick)
def runner_SS(output_base, baseline_dir, baseline=False, analytical_mtrs=True, age_specific=False, reform={}, user_params={}, guid='', run_micro=True):
from ogusa import parameters, wealth, labor, demographics, income
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific,
start_year=user_params['start_year'], reform=reform, guid=guid)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
globals().update(run_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir=output_base)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir=output_base)
get_baseline = True
calibrate_model = True
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq', 'calibrate_model',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth', 'get_baseline',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho']
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
glbs = globals()
lcls = locals()
for key in param_names:
if key in glbs:
sim_params[key] = glbs[key]
else:
sim_params[key] = lcls[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_steady_state(income_tax_params, ss_parameters, iterative_params, baseline,
calibrate_model, output_dir=output_base, baseline_dir=baseline_dir)
def runner(output_base,
baseline_dir,
baseline=False,
analytical_mtrs=True,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, wealth, labor, demog, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params['start_year'],
reform=reform,
guid=guid)
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(run_params['lambdas'],
run_params['J'],
run_params['flag_graphs'],
output_dir=output_base)
# Generate labor data moments
labor.labor_data_moments(run_params['flag_graphs'], output_dir=output_base)
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'analytical_mtrs', 'b_ellipse',
'k_ellipse', 'upsilon', 'chi_b_guess', 'chi_n_guess', 'etr_params',
'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq', 'retire',
'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho'
]
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_SS(income_tax_params,
ss_parameters,
iterative_params,
chi_params,
baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_dir, "SS"))
ss_dir = os.path.join(output_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, initial_values, SS_values = TPI.create_tpi_params(
**sim_params)
# ss_outputs['income_tax_params'] = income_tax_params
# ss_outputs['wealth_tax_params'] = wealth_tax_params
# ss_outputs['ellipse_params'] = ellipse_params
# ss_outputs['parameters'] = parameters
# ss_outputs['N_tilde'] = N_tilde
# ss_outputs['omega_stationary'] = omega_stationary
# ss_outputs['K0'] = K0
# ss_outputs['b_sinit'] = b_sinit
# ss_outputs['b_splus1init'] = b_splus1init
# ss_outputs['L0'] = L0
# ss_outputs['Y0'] = Y0
# ss_outputs['r0'] = r0
# ss_outputs['BQ0'] = BQ0
# ss_outputs['T_H_0'] = T_H_0
# ss_outputs['factor_ss'] = factor
# ss_outputs['tax0'] = tax0
# ss_outputs['c0'] = c0
# ss_outputs['initial_b'] = initial_b
# ss_outputs['initial_n'] = initial_n
# ss_outputs['tau_bq'] = tau_bq
# ss_outputs['g_n_vector'] = g_n_vector
# ss_outputs['output_dir'] = output_base
# with open("ss_outputs.pkl", 'wb') as fp:
# pickle.dump(ss_outputs, fp)
w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_TPI(
income_tax_params,
tpi_params,
iterative_params,
initial_values,
SS_values,
output_dir=output_base)
print "getting to here...."
TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - tick)
def runner_SS(
output_base,
baseline_dir,
baseline=False,
analytical_mtrs=False,
age_specific=False,
reform={},
user_params={},
guid="",
run_micro=True,
):
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
# Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(
baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params["start_year"],
reform=reform,
guid=guid,
)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params["analytical_mtrs"] = analytical_mtrs
# Modify ogusa parameters based on user input
if "frisch" in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params["frisch"], run_params["ltilde"])
run_params["b_ellipse"] = b_ellipse
run_params["upsilon"] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if "g_y_annual" in user_params:
print "updating g_y_annual and associated"
ending_age = run_params["ending_age"]
starting_age = run_params["starting_age"]
S = run_params["S"]
g_y = (1 + user_params["g_y_annual"]) ** (float(ending_age - starting_age) / S) - 1
run_params["g_y"] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
"""
****
CALL CALIBRATION here if boolean flagged
****
"""
calibrate_model = False
# if calibrate_model:
# chi_b, chi_n = calibrate.(income_tax_params, ss_params, iterative_params, chi_params, baseline,
# calibrate_model, output_dir=output_base, baseline_dir=baseline_dir)
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
"S",
"J",
"T",
"BW",
"lambdas",
"starting_age",
"ending_age",
"beta",
"sigma",
"alpha",
"nu",
"Z",
"delta",
"E",
"ltilde",
"g_y",
"maxiter",
"mindist_SS",
"mindist_TPI",
"analytical_mtrs",
"b_ellipse",
"k_ellipse",
"upsilon",
"chi_b_guess",
"chi_n_guess",
"etr_params",
"mtrx_params",
"mtry_params",
"tau_payroll",
"tau_bq",
"retire",
"mean_income_data",
"g_n_vector",
"h_wealth",
"p_wealth",
"m_wealth",
"omega",
"g_n_ss",
"omega_SS",
"surv_rate",
"imm_rates",
"e",
"rho",
"omega_S_preTP",
]
"""
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
"""
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params["output_dir"] = output_base
sim_params["run_params"] = run_params
income_tax_params, ss_params, iterative_params, chi_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_SS(
income_tax_params, ss_params, iterative_params, chi_params, baseline, baseline_dir=baseline_dir
)
"""
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
"""
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
def runner(output_base, baseline_dir, test=False, time_path=True, baseline=False,
analytical_mtrs=False, age_specific=False, reform={}, user_params={},
guid='', run_micro=True, small_open=False, budget_balance=False, baseline_spending=False):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
# Make sure options are internally consistent
if baseline==True and baseline_spending==True:
print 'Inconsistent options. Setting <baseline_spending> to False, leaving <baseline> True.'
baseline_spending = False
if budget_balance==True and baseline_spending==True:
print 'Inconsistent options. Setting <baseline_spending> to False, leaving <budget_balance> True.'
baseline_spending = False
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific,
start_year=user_params['start_year'], reform=reform, guid=guid)
print 'In runner, baseline is ', baseline
run_params = ogusa.parameters.get_parameters(test=test, baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
run_params['small_open'] = small_open
run_params['budget_balance'] = budget_balance
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating frisch and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
if 'debt_ratio_ss' in user_params:
run_params['debt_ratio_ss']=user_params['debt_ratio_ss']
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
ending_age = run_params['ending_age']
starting_age = run_params['starting_age']
S = run_params['S']
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
# Modify transfer & spending ratios based on user input.
if 'T_shifts' in user_params:
if baseline_spending==False:
print 'updating ALPHA_T with T_shifts in first', user_params['T_shifts'].size, 'periods.'
T_shifts = np.concatenate((user_params['T_shifts'], np.zeros(run_params['ALPHA_T'].size - user_params['T_shifts'].size)), axis=0)
run_params['ALPHA_T'] = run_params['ALPHA_T'] + T_shifts
if 'G_shifts' in user_params:
if baseline_spending==False:
print 'updating ALPHA_G with G_shifts in first', user_params['G_shifts'].size, 'periods.'
G_shifts = np.concatenate((user_params['G_shifts'], np.zeros(run_params['ALPHA_G'].size - user_params['G_shifts'].size)), axis=0)
run_params['ALPHA_G'] = run_params['ALPHA_G'] + G_shifts
from ogusa import SS, TPI
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'gamma', 'epsilon', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'small_open', 'budget_balance', 'ss_firm_r', 'ss_hh_r', 'tpi_firm_r', 'tpi_hh_r',
'tG1', 'tG2', 'alpha_T', 'alpha_G', 'ALPHA_T', 'ALPHA_G', 'rho_G', 'debt_ratio_ss',
'tau_b', 'delta_tau',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'imm_rates','e', 'rho',
'initial_debt','omega_S_preTP']
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params, small_open_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_SS(income_tax_params, ss_parameters, iterative_params, chi_params, small_open_params, baseline, baseline_spending,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
sim_params['baseline'] = baseline
sim_params['baseline_spending'] = baseline_spending
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, small_open_params, initial_values, SS_values, fiscal_params, biz_tax_params = TPI.create_tpi_params(**sim_params)
tpi_output, macro_output = TPI.run_TPI(income_tax_params, tpi_params, iterative_params, small_open_params, initial_values,
SS_values, fiscal_params, biz_tax_params, output_dir=output_base, baseline_spending=baseline_spending)
'''
------------------------------------------------------------------------
Pickle TPI results
------------------------------------------------------------------------
'''
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(tpi_output, open(tpi_vars, "wb"))
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_macro_vars.pkl")
pickle.dump(macro_output, open(tpi_vars, "wb"))
print "Time path iteration complete."
print "It took {0} seconds to get that part done.".format(time.time() - tick)
