def runner():
#Create output directory structure
dirs = ["./OUTPUT/Saved_moments", "./OUTPUT/SSinit", "./OUTPUT/TPIinit"]
for _dir in dirs:
try:
os.makedirs(_dir)
except OSError as oe:
pass
# Generate Wealth data moments
output_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir)
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', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess', 'b_tax_income',
'c_tax_income', 'd_tax_income', '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]
income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters(
**sim_params)
print "got here"
before = time.time()
ss_outputs = SS.run_steady_state(ss_parameters, iterative_params,
get_baseline)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
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
with open("ss_outputs.pkl", 'wb') as fp:
pickle.dump(ss_outputs, fp)
TPI.run_time_path_iteration(**ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() -
before)
def test_sstpi():
import tempfile
import pickle
import numpy as np
import numpy as np
import cPickle as pickle
import os
import dynamic
dynamic.parameters.DATASET = 'REAL'
from dynamic.utils import comp_array
from dynamic.utils import comp_scalar
from dynamic.utils import dict_compare
from dynamic.utils import pickle_file_compare
import dynamic.SS
import dynamic.TPI
from dynamic import parameters, wealth, labor, demographics, income, SS, TPI
globals().update(dynamic.parameters.get_parameters())
# Generate Wealth data moments
output_dir = TEST_OUTPUT
input_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir=output_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', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess', 'b_tax_income',
'c_tax_income', 'd_tax_income', '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 = {}
for key in param_names:
try:
sim_params[key] = locals()[key]
except KeyError:
sim_params[key] = globals()[key]
sim_params['output_dir'] = output_dir
sim_params['input_dir'] = input_dir
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(ss_parameters,
iterative_params,
get_baseline,
calibrate_model,
output_dir=output_dir)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
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['output_dir'] = output_dir
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
TPI.run_time_path_iteration(**ss_outputs)
# Platform specific exceptions:
if sys.platform == "darwin":
exceptions = {
'tax_path': 0.08,
'c_path': 0.02,
'b_mat': 0.0017,
'solutions': 0.005
}
else:
exceptions = {}
# compare results to test data
for old, new in zip(oldfiles, newfiles):
print "trying a pair"
print old, new
assert pickle_file_compare(old,
new,
exceptions=exceptions,
relative=True)
print "next pair"
from dynamic import parameters, wealth, labor, demographics, income, SS, TPI
'''
------------------------------------------------------------------------
Setup
------------------------------------------------------------------------
get_baseline = Flag to run baseline or tax experiments (bool)
calibrate_model = Flag to run calibration of chi values or not (bool)
------------------------------------------------------------------------
'''
globals().update(dynamic.parameters.get_parameters())
# Generate Wealth data moments
output_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs)
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', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess',
'b_tax_income', 'c_tax_income', 'd_tax_income',
import dynamic.TPI
from dynamic import parameters, wealth, labor, demographics, income, SS, TPI
'''
------------------------------------------------------------------------
Setup
------------------------------------------------------------------------
get_baseline = Flag to run baseline or tax experiments (bool)
calibrate_model = Flag to run calibration of chi values or not (bool)
------------------------------------------------------------------------
'''
globals().update(dynamic.parameters.get_parameters())
# Generate Wealth data moments
output_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs)
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', 'lambdas', 'starting_age', 'ending_age', 'beta', 'sigma',
'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter', 'mindist_SS',
'mindist_TPI', 'b_ellipse', 'k_ellipse', 'upsilon', 'a_tax_income',
'chi_b_guess', 'chi_n_guess', 'b_tax_income', 'c_tax_income',
'd_tax_income', 'tau_payroll', 'tau_bq', 'calibrate_model', 'retire',
def runner():
#Create output directory structure
dirs = ["./OUTPUT/Saved_moments", "./OUTPUT/SSinit", "./OUTPUT/TPIinit"]
for _dir in dirs:
try:
os.makedirs(_dir)
except OSError as oe:
pass
# Generate Wealth data moments
output_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir)
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', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess',
'b_tax_income', 'c_tax_income', 'd_tax_income',
'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]
income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters(**sim_params)
print "got here"
before = time.time()
ss_outputs = SS.run_steady_state(ss_parameters, iterative_params, get_baseline)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
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
with open("ss_outputs.pkl", 'wb') as fp:
pickle.dump(ss_outputs, fp)
TPI.run_time_path_iteration(**ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - before)
def test_sstpi():
import tempfile
import pickle
import numpy as np
import numpy as np
import cPickle as pickle
import os
import dynamic
dynamic.parameters.DATASET = 'REAL'
from dynamic.utils import comp_array
from dynamic.utils import comp_scalar
from dynamic.utils import dict_compare
from dynamic.utils import pickle_file_compare
import dynamic.SS
import dynamic.TPI
from dynamic import parameters, wealth, labor, demographics, income, SS, TPI
globals().update(dynamic.parameters.get_parameters())
# Generate Wealth data moments
output_dir = TEST_OUTPUT
input_dir = "./OUTPUT"
wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir)
# Generate labor data moments
labor.labor_data_moments(flag_graphs, output_dir=output_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', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess',
'b_tax_income', 'c_tax_income', 'd_tax_income',
'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 = {}
for key in param_names:
try:
sim_params[key] = locals()[key]
except KeyError:
sim_params[key] = globals()[key]
sim_params['output_dir'] = output_dir
sim_params['input_dir'] = input_dir
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(ss_parameters, iterative_params,
get_baseline, calibrate_model,
output_dir=output_dir)
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
ss_outputs['get_baseline'] = get_baseline
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['output_dir'] = output_dir
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
TPI.run_time_path_iteration(**ss_outputs)
# Platform specific exceptions:
if sys.platform == "darwin":
exceptions = {'tax_path': 0.08,
'c_path': 0.02,
'b_mat': 0.0017,
'solutions': 0.005}
else:
exceptions = {}
# compare results to test data
for old, new in zip(oldfiles, newfiles):
print "trying a pair"
print old, new
assert pickle_file_compare(
old, new, exceptions=exceptions, relative=True)
print "next pair"