def runner(self):
'''
Method to run the model
'''
# baseline compute
self.base_ss_outputs = SS.run_SS(self.spec1)
if self.spec1.time_path:
self.base_tpi_output = TPI.run_TPI(self.spec1)
# reform compute
# a bit more complicated because will need stuff from baseline
self.reform_ss_outputs = SS.run_SS(self.spec2)
if self.spec2.time_path:
self.reform_tpi_output = TPI.run_TPI(self.spec2)
def minstat(beta_guesses, *args):
'''
This function generates the weighted sum of squared differences
between the model and data moments.
Args:
beta_guesses (array-like): a vector of length J with the betas
args (tuple): length 6 tuple, variables needed for minimizer
Returns:
distance (scalar): weighted, squared deviation between data and
model moments
'''
# unpack args tuple
data_moments, W, p, client = args
# Update beta in parameters object with beta guesses
p.beta = beta_guesses
# Solve model SS
print('Baseline = ', p.baseline)
ss_output = SS.run_SS(p, client=client)
# Compute moments from model SS
model_moments = calc_moments(ss_output, p)
# distance with levels
distance = np.dot(
np.dot((np.array(model_moments) - np.array(data_moments)).T, W),
np.array(model_moments) - np.array(data_moments))
print('DATA and MODEL DISTANCE: ', distance)
return distance
def find_moments(p, client):
b_guess = np.ones((p.S, p.J)) * 0.07
n_guess = np.ones((p.S, p.J)) * .4 * p.ltilde
rguess = 0.08961277823002804 # 0.09
T_Hguess = 0.12
factorguess = 12.73047710050195 # 7.7 #70000 # Modified
BQguess = aggr.get_BQ(rguess, b_guess, None, p, 'SS', False)
exit_early = [0, -1] # 2nd value gives number of valid labor moments to consider before exiting SS_fsolve
# Put -1 to run to SS
ss_params_baseline = (b_guess, n_guess, None, None, p, client, exit_early)
guesses = [rguess] + list(BQguess) + [T_Hguess, factorguess]
[solutions_fsolve, infodict, ier, message] =\
opt.fsolve(SS.SS_fsolve, guesses, args=ss_params_baseline,
xtol=p.mindist_SS, full_output=True)
rss = solutions_fsolve[0]
BQss = solutions_fsolve[1:-2]
T_Hss = solutions_fsolve[-2]
factor_ss = solutions_fsolve[-1]
Yss = T_Hss/p.alpha_T[-1]
fsolve_flag = True
try:
output = SS.SS_solver(b_guess, n_guess, rss, BQss, T_Hss,
factor_ss, Yss, p, client, fsolve_flag)
except:
print('RuntimeError: Steady state aggregate resource constraint not satisfied')
print('Luckily we caught the error, so minstat_init_calibrate will continue')
return 1e10
model_moments = np.array(output['nssmat'].mean(axis=1)[:45]) # calc_moments(output, p.omega_SS, p.lambdas, p.S, p.J)
return model_moments
def test_run_SS(input_path, expected_path):
# Test SS.run_SS function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', input_path))
(income_tax_params, ss_params, iterative_params, chi_params,
small_open_params, baseline, baseline_spending, baseline_dir) =\
input_tuple
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z,
p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll, tau_bq, p.rho,
p.omega_SS, p.budget_balance, alpha_T, p.debt_ratio_ss, tau_b, delta_tau,
lambdas, imm_rates, p.e, retire, p.mean_income_data, h_wealth, p_wealth,
m_wealth, p.b_ellipse, p.upsilon) = ss_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.baseline_dir = baseline_dir
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(
etr_params.reshape(p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(
mtrx_params.reshape(p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(
mtry_params.reshape(p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.maxiter, p.mindist_SS = iterative_params
p.chi_b, p.chi_n = chi_params
p.small_open, ss_firm_r, ss_hh_r = small_open_params
p.ss_firm_r = np.ones(p.T + p.S) * ss_firm_r
p.ss_hh_r = np.ones(p.T + p.S) * ss_hh_r
p.num_workers = 1
test_dict = SS.run_SS(p, None)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', expected_path))
# delete values key-value pairs that are not in both dicts
del expected_dict['bssmat'], expected_dict['chi_n'], expected_dict['chi_b']
del test_dict['etr_ss'], test_dict['mtrx_ss'], test_dict['mtry_ss']
test_dict['IITpayroll_revenue'] = (test_dict['total_revenue_ss'] -
test_dict['business_revenue'])
del test_dict['T_Pss'], test_dict['T_BQss'], test_dict['T_Wss']
del test_dict['resource_constraint_error'], test_dict['T_Css']
test_dict['revenue_ss'] = test_dict.pop('total_revenue_ss')
for k, v in expected_dict.items():
assert (np.allclose(test_dict[k], v))
def test_SS_solver(baseline, param_updates, filename, dask_client):
# Test SS.SS_solver function. Provide inputs to function and
# ensure that output returned matches what it has been before.
p = Specifications(baseline=baseline,
client=dask_client,
num_workers=NUM_WORKERS)
p.update_specifications(param_updates)
p.output_base = CUR_PATH
p.get_tax_function_parameters(None, run_micro=False)
b_guess = np.ones((p.S, p.J)) * 0.07
n_guess = np.ones((p.S, p.J)) * .35 * p.ltilde
if p.zeta_K[-1] == 1.0:
rguess = p.world_int_rate[-1]
else:
rguess = 0.06483431412921253
TRguess = 0.05738932081035772
factorguess = 139355.1547340256
BQguess = aggregates.get_BQ(rguess, b_guess, None, p, 'SS', False)
Yguess = 0.6376591201150815
test_dict = SS.SS_solver(b_guess, n_guess, rguess, BQguess, TRguess,
factorguess, Yguess, p, None, False)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', filename))
for k, v in expected_dict.items():
print('Testing ', k)
assert (np.allclose(test_dict[k], v, atol=1e-07, equal_nan=True))
def test_run_SS(baseline, param_updates, filename, dask_client):
# Test SS.run_SS function. Provide inputs to function and
# ensure that output returned matches what it has been before.
if baseline is False:
tax_func_path_baseline = os.path.join(CUR_PATH,
'TxFuncEst_baseline.pkl')
tax_func_path = os.path.join(CUR_PATH, 'TxFuncEst_policy.pkl')
execute.runner(constants.BASELINE_DIR,
constants.BASELINE_DIR,
time_path=False,
baseline=True,
og_spec=param_updates,
run_micro=False,
tax_func_path=tax_func_path_baseline)
else:
tax_func_path = os.path.join(CUR_PATH, 'TxFuncEst_baseline.pkl')
p = Specifications(baseline=baseline,
client=dask_client,
num_workers=NUM_WORKERS)
p.update_specifications(param_updates)
p.get_tax_function_parameters(None,
run_micro=False,
tax_func_path=tax_func_path)
test_dict = SS.run_SS(p, None)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', filename))
for k, v in expected_dict.items():
assert (np.allclose(test_dict[k], v))
def test_SS_fsolve(guesses, args, expected):
'''
Test SS.SS_fsolve function. Provide inputs to function and
ensure that output returned matches what it has been before.
'''
test_list = SS.SS_fsolve(guesses, *args)
assert (np.allclose(np.array(test_list), np.array(expected), atol=1e-6))
def test_inner_loop(baseline, param_updates, filename, dask_client):
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
p = Specifications(baseline=baseline,
client=dask_client,
num_workers=NUM_WORKERS)
p.update_specifications(param_updates)
p.output_base = CUR_PATH
p.get_tax_function_parameters(None, run_micro=False)
bssmat = np.ones((p.S, p.J)) * 0.07
nssmat = np.ones((p.S, p.J)) * .4 * p.ltilde
if p.zeta_K[-1] == 1.0:
r = p.world_int_rate[-1]
else:
r = 0.05
TR = 0.12
Y = 1.3
factor = 100000
BQ = np.ones(p.J) * 0.00019646295986015257
if p.budget_balance:
outer_loop_vars = (bssmat, nssmat, r, BQ, TR, factor)
else:
outer_loop_vars = (bssmat, nssmat, r, BQ, Y, TR, factor)
test_tuple = SS.inner_loop(outer_loop_vars, p, None)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', filename))
for i, v in enumerate(expected_tuple):
assert (np.allclose(test_tuple[i], v, atol=1e-05))
def minstat_init_calibrate(params, *args):
a0, a1, a2, a3, a4 = params
p, client, data_moments, W, ages = args
chi_n = np.ones(p.S)
chi_n[:p.S // 2 + 5] = chebyshev_func(ages, a0, a1, a2, a3, a4)
slope = chi_n[p.S // 2 + 5 - 1] - chi_n[p.S // 2 + 5 - 2]
chi_n[p.S // 2 + 5 -
1:] = (np.linspace(65, 100, 36) - 65) * slope + chi_n[p.S // 2 + 5 -
1]
chi_n[chi_n < 0.5] = 0.5
p.chi_n = chi_n
print("-----------------------------------------------------")
print('PARAMS AT START' + str(params))
print("-----------------------------------------------------")
b_guess = np.ones((p.S, p.J)) * 0.07
n_guess = np.ones((p.S, p.J)) * .4 * p.ltilde
rguess = 0.09
T_Hguess = 0.12
factorguess = 7.7 #70000 # Modified
BQguess = aggr.get_BQ(rguess, b_guess, None, p, 'SS', False)
exit_early = [
0, 2
] # 2nd value gives number of valid labor moments to consider before exiting SS_fsolve
ss_params_baseline = (b_guess, n_guess, None, None, p, client, exit_early)
guesses = [rguess] + list(BQguess) + [T_Hguess, factorguess]
[solutions_fsolve, infodict, ier, message] =\
opt.fsolve(SS.SS_fsolve, guesses, args=ss_params_baseline,
xtol=p.mindist_SS, full_output=True)
rss = solutions_fsolve[0]
BQss = solutions_fsolve[1:-2]
T_Hss = solutions_fsolve[-2]
factor_ss = solutions_fsolve[-1]
Yss = T_Hss / p.alpha_T[-1]
fsolve_flag = True
try:
output = SS.SS_solver(b_guess, n_guess, rss, BQss, T_Hss, factor_ss,
Yss, p, client, fsolve_flag)
except:
print(
'RuntimeError: Steady state aggregate resource constraint not satisfied'
)
print(
'Luckily we caught the error, so minstat_init_calibrate will continue'
)
return 1e10
model_moments = calc_moments(output, p.omega_SS, p.lambdas, p.S, p.J)
print('Model moments:', model_moments)
print("-----------------------------------------------------")
distance = np.dot(
np.dot((np.array(model_moments[:9]) - np.array(data_moments)).T, W),
np.array(model_moments[:9]) - np.array(data_moments))
print('DATA and MODEL DISTANCE: ', distance)
return distance
def test_inner_loop():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_inputs.pkl'))
(outer_loop_vars_in, params, baseline, baseline_spending) = input_tuple
ss_params, income_tax_params, chi_params, small_open_params = params
(bssmat, nssmat, r, Y, T_H, factor) = outer_loop_vars_in
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
BQ = np.ones(p.J) * 0.00019646295986015257
outer_loop_vars = (bssmat, nssmat, r, BQ, Y, T_H, factor)
(euler_errors, new_bmat, new_nmat, new_r, new_r_gov, new_r_hh,
new_w, new_T_H, new_Y, new_factor, new_BQ,
average_income_model) = SS.inner_loop(outer_loop_vars, p, None)
test_tuple = (euler_errors, new_bmat, new_nmat, new_r, new_w,
new_T_H, new_Y, new_factor, new_BQ,
average_income_model)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert(np.allclose(test_tuple[i], v, atol=1e-05))
def test_inner_loop():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_inputs.pkl'))
(outer_loop_vars_in, params, baseline, baseline_spending) = input_tuple
ss_params, income_tax_params, chi_params, small_open_params = params
(bssmat, nssmat, r, Y, T_H, factor) = outer_loop_vars_in
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
BQ = np.ones(p.J) * 0.00019646295986015257
outer_loop_vars = (bssmat, nssmat, r, BQ, Y, T_H, factor)
(euler_errors, new_bmat, new_nmat, new_r, new_r_gov, new_r_hh,
new_w, new_T_H, new_Y, new_factor, new_BQ,
average_income_model) = SS.inner_loop(outer_loop_vars, p, None)
test_tuple = (euler_errors, new_bmat, new_nmat, new_r, new_w,
new_T_H, new_Y, new_factor, new_BQ,
average_income_model)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert(np.allclose(test_tuple[i], v, atol=1e-05))
def test_constant_demographics_TPI():
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time.
In this case, given how initial guesss for the time
path are made, the time path should be solved for on the first
iteration and the values all along the time path should equal their
steady-state values.
'''
output_base = "./OUTPUT"
baseline_dir = "./OUTPUT"
user_params = {
'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2
}
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError as oe:
pass
spec = Specifications(run_micro=False,
output_base=output_base,
baseline_dir=baseline_dir,
test=False,
time_path=True,
baseline=True,
reform={},
guid='')
spec.update_specifications(user_params)
print('path for tax functions: ', spec.output_base)
spec.get_tax_function_parameters(None, False)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
tpi_output = TPI.run_TPI(spec, None)
print(
'Max diff btwn SS and TP bsplus1 = ',
np.absolute(tpi_output['bmat_splus1'][:spec.T, :, :] -
ss_outputs['bssmat_splus1']).max())
print('Max diff btwn SS and TP Y = ',
np.absolute(tpi_output['Y'][:spec.T] - ss_outputs['Yss']).max())
assert (np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def test_run_TPI(baseline, param_updates, filename, tmp_path, dask_client):
'''
Test TPI.run_TPI function. Provide inputs to function and
ensure that output returned matches what it has been before.
'''
baseline_dir = os.path.join(CUR_PATH, 'baseline')
if baseline:
output_base = baseline_dir
else:
output_base = os.path.join(CUR_PATH, 'reform')
p = Specifications(baseline=baseline,
baseline_dir=baseline_dir,
output_base=output_base,
test=True,
client=dask_client,
num_workers=NUM_WORKERS)
p.update_specifications(param_updates)
p.maxiter = 2 # this test runs through just two iterations
p.get_tax_function_parameters(None,
run_micro=False,
tax_func_path=os.path.join(
CUR_PATH, '..', 'data', 'tax_functions',
'TxFuncEst_baseline_CPS.pkl'))
# Need to run SS first to get results
SS.ENFORCE_SOLUTION_CHECKS = False
ss_outputs = SS.run_SS(p, None)
if p.baseline:
utils.mkdirs(os.path.join(p.baseline_dir, "SS"))
ss_dir = os.path.join(p.baseline_dir, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
else:
utils.mkdirs(os.path.join(p.output_base, "SS"))
ss_dir = os.path.join(p.output_base, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
TPI.ENFORCE_SOLUTION_CHECKS = False
test_dict = TPI.run_TPI(p, None)
expected_dict = utils.safe_read_pickle(filename)
for k, v in expected_dict.items():
try:
assert (np.allclose(test_dict[k][:p.T],
v[:p.T],
rtol=1e-04,
atol=1e-04))
except ValueError:
assert (np.allclose(test_dict[k][:p.T, :, :],
v[:p.T, :, :],
rtol=1e-04,
atol=1e-04))
def compute_se(beta_hat, W, K, p, h=0.01, client=None):
"""
Function to compute standard errors for the SMM estimator.
Args:
beta_hat (array-like): estimates of beta parameters
W (Numpy array): weighting matrix
K (int): number of moments
p (OG-USA Specifications object): model parameters
h (scalar): percentage to move parameters for numerical derivatives
client (Dask Client object): Dask client
Returns:
beta_se (array-like): standard errors for beta estimates
VCV_params (Numpy array): VCV matrix for parameter estimates
"""
# compute numerical derivatives that will need for SE's
model_moments_low = np.zeros((p.J, K))
model_moments_high = np.zeros((p.J, K))
beta_low = beta_hat
beta_high = beta_hat
for i in range(len(beta_hat)):
# compute moments with downward change in param
beta_low[i] = beta_hat[i] * (1 + h)
p.beta = beta_low
ss_output = ss_output = SS.run_SS(p, client=client)
model_moments_low[i, :] = calc_moments(ss_output, p)
# compute moments with upward change in param
beta_high[i] = beta_hat[i] * (1 - h)
p.beta = beta_low
ss_output = ss_output = SS.run_SS(p, client=client)
model_moments_high[i, :] = calc_moments(ss_output, p)
deriv_moments = (model_moments_high - model_moments_low).T / (2 * h *
beta_hat)
VCV_params = np.linalg.inv(
np.dot(np.dot(deriv_moments.T, W), deriv_moments))
beta_se = (np.diag(VCV_params))**(1 / 2)
return beta_se, VCV_params
def test_constant_demographics_TPI(dask_client):
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time.
In this case, given how initial guesss for the time
path are made, the time path should be solved for on the first
iteration and the values all along the time path should equal their
steady-state values.
'''
# Create output directory structure
spec = Specifications(run_micro=False,
output_base=OUTPUT_DIR,
baseline_dir=OUTPUT_DIR,
test=False,
time_path=True,
baseline=True,
iit_reform={},
guid='',
client=dask_client,
num_workers=NUM_WORKERS)
og_spec = {
'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2,
'r_gov_shift': 0.0,
'zeta_D': [0.0, 0.0],
'zeta_K': [0.0, 0.0],
'debt_ratio_ss': 1.0,
'initial_foreign_debt_ratio': 0.0,
'start_year': 2019,
'cit_rate': [0.0],
'PIA_rate_bkt_1': 0.0,
'PIA_rate_bkt_2': 0.0,
'PIA_rate_bkt_3': 0.0,
'eta':
(spec.omega_SS.reshape(spec.S, 1) * spec.lambdas.reshape(1, spec.J))
}
spec.update_specifications(og_spec)
spec.get_tax_function_parameters(None, False, tax_func_path=TAX_FUNC_PATH)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
utils.mkdirs(os.path.join(OUTPUT_DIR, "SS"))
ss_dir = os.path.join(OUTPUT_DIR, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
# Run TPI
tpi_output = TPI.run_TPI(spec, None)
assert (np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def test_constant_demographics_TPI():
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time.
In this case, given how initial guesss for the time
path are made, the time path should be solved for on the first
iteration and the values all along the time path should equal their
steady-state values.
'''
output_base = "./OUTPUT"
baseline_dir = "./OUTPUT"
user_params = {'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2}
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError as oe:
pass
spec = Specifications(run_micro=False, output_base=output_base,
baseline_dir=baseline_dir, test=False,
time_path=True, baseline=True, reform={},
guid='')
spec.update_specifications(user_params)
print('path for tax functions: ', spec.output_base)
spec.get_tax_function_parameters(None, False)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
tpi_output = TPI.run_TPI(spec, None)
print('Max diff btwn SS and TP bsplus1 = ',
np.absolute(tpi_output['bmat_splus1'][:spec.T, :, :] -
ss_outputs['bssmat_splus1']).max())
print('Max diff btwn SS and TP Y = ',
np.absolute(tpi_output['Y'][:spec.T] -
ss_outputs['Yss']).max())
assert(np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def test_constant_demographics_TPI():
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time.
In this case, given how initial guesss for the time
path are made, the time path should be solved for on the first
iteration and the values all along the time path should equal their
steady-state values.
'''
output_base = os.path.join(CUR_PATH, 'OUTPUT')
baseline_dir = output_base
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError:
pass
spec = Specifications(run_micro=False,
output_base=output_base,
baseline_dir=baseline_dir,
test=False,
time_path=True,
baseline=True,
iit_reform={},
guid='')
og_spec = {
'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2,
'eta':
(spec.omega_SS.reshape(spec.S, 1) * spec.lambdas.reshape(1, spec.J))
}
spec.update_specifications(og_spec)
spec.get_tax_function_parameters(None, False)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
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"))
# Run TPI
tpi_output = TPI.run_TPI(spec, None)
assert (np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def test_constant_demographics_TPI_small_open():
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time, as well as with a small open economy assumption.
'''
# Create output directory structure
spec = Specifications(run_micro=False,
output_base=OUTPUT_DIR,
baseline_dir=OUTPUT_DIR,
test=False,
time_path=True,
baseline=True,
iit_reform={},
guid='')
og_spec = {
'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2,
'r_gov_shift': 0.0,
'zeta_D': [0.0, 0.0],
'zeta_K': [1.0],
'debt_ratio_ss': 1.0,
'initial_foreign_debt_ratio': 0.0,
'start_year': 2019,
'cit_rate': [0.0],
'PIA_rate_bkt_1': 0.0,
'PIA_rate_bkt_2': 0.0,
'PIA_rate_bkt_3': 0.0,
'eta':
(spec.omega_SS.reshape(spec.S, 1) * spec.lambdas.reshape(1, spec.J))
}
spec.update_specifications(og_spec)
spec.get_tax_function_parameters(None, False, tax_func_path=TAX_FUNC_PATH)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
utils.mkdirs(os.path.join(OUTPUT_DIR, "SS"))
ss_dir = os.path.join(OUTPUT_DIR, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
# Run TPI
tpi_output = TPI.run_TPI(spec, None)
assert (np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def test_inner_loop():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_inputs.pkl'))
(outer_loop_vars, params, baseline, baseline_spending) = input_tuple
ss_params, income_tax_params, chi_params, small_open_params = params
income_tax_params = ('DEP', ) + income_tax_params
params = (ss_params, income_tax_params, chi_params, small_open_params)
test_tuple = SS.inner_loop(outer_loop_vars, params, baseline,
baseline_spending)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert (np.allclose(test_tuple[i], v))
def test_SS_fsolve_reform():
# Test SS.SS_fsolve_reform function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_inputs.pkl'))
guesses, params = input_tuple
params = params + (None, 1)
(bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, factor, small_open_params, client, num_workers) = params
income_tax_params = ('DEP', ) + income_tax_params
params = (bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, factor, small_open_params, client, num_workers)
test_list = SS.SS_fsolve_reform(guesses, params)
expected_list = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_outputs.pkl'))
assert (np.allclose(np.array(test_list), np.array(expected_list)))
def test_run_SS(input_path, expected_path):
# Test SS.run_SS function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', input_path))
(income_tax_params, ss_params, iterative_params, chi_params,
small_open_params, baseline, baseline_spending, baseline_dir) =\
input_tuple
income_tax_params = ('DEP', ) + income_tax_params
test_dict = SS.run_SS(income_tax_params, ss_params, iterative_params,
chi_params, small_open_params, baseline,
baseline_spending, baseline_dir)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', expected_path))
for k, v in expected_dict.items():
assert (np.allclose(test_dict[k], v))
def test_SS_solver():
# Test SS.SS_solver function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_solver_inputs.pkl'))
(b_guess_init, n_guess_init, rss, T_Hss, factor_ss, Yss, params, baseline,
fsolve_flag, baseline_spending) = input_tuple
(bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, small_open_params) = params
income_tax_params = ('DEP', ) + income_tax_params
params = (bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, small_open_params)
test_dict = SS.SS_solver(b_guess_init, n_guess_init, rss, T_Hss, factor_ss,
Yss, params, baseline, fsolve_flag,
baseline_spending)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_solver_outputs.pkl'))
for k, v in expected_dict.items():
assert (np.allclose(test_dict[k], v))
def matcher(d_guess, params):
income_tax_params, receipts_to_match, ss_params, iterative_params,\
chi_params, baseline, baseline_dir = params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
etr_params[:, 3] = d_guess
mtrx_params[:, 3] = d_guess
mtry_params[:, 3] = d_guess
income_tax_params = analytical_mtrs, etr_params, mtrx_params, mtry_params
ss_outputs = SS.run_SS(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline,
fix_transfers=fix_transfers,
baseline_dir=baseline_dir)
receipts_new = ss_outputs['T_Hss'] + ss_outputs['Gss']
error = abs(receipts_to_match - receipts_new)
if d_guess <= 0:
error = 1e14
print 'Error in taxes:', error
return error
def test_euler_equation_solver():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_inputs.pkl'))
(guesses, params) = input_tuple
(r, w, T_H, factor, j, J, S, beta, sigma, ltilde, g_y, g_n_ss, tau_payroll,
retire, mean_income_data, h_wealth, p_wealth, m_wealth, b_ellipse,
upsilon, j, chi_b, chi_n, tau_bq, rho, lambdas, omega_SS, e,
analytical_mtrs, etr_params, mtrx_params, mtry_params) = params
tax_func_type = 'DEP'
params = (r, w, T_H, factor, j, J, S, beta, sigma, ltilde, g_y, g_n_ss,
tau_payroll, retire, mean_income_data, h_wealth, p_wealth,
m_wealth, b_ellipse, upsilon, j, chi_b, chi_n, tau_bq, rho,
lambdas, omega_SS, e, tax_func_type, analytical_mtrs, etr_params,
mtrx_params, mtry_params)
test_list = SS.euler_equation_solver(guesses, params)
expected_list = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_outputs.pkl'))
assert (np.allclose(np.array(test_list), np.array(expected_list)))
def solve_model(params, d):
income_tax_params, ss_params, iterative_params,\
chi_params, baseline ,baseline_dir = params
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
etr_params[:, 3] = d
mtrx_params[:, 3] = d
mtry_params[:, 3] = d
income_tax_params = analytical_mtrs, etr_params, mtrx_params, mtry_params
ss_outputs = SS.run_SS(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline,
fix_transfers=fix_transfers,
baseline_dir=baseline_dir)
ss_dir = os.path.join("./OUTPUT_INCOME_REFORM/sigma2.0",
"SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
receipts_new = ss_outputs['T_Hss'] + ss_outputs['Gss']
new_error = receipts_to_match - receipts_new
print 'Error in taxes:', error
print 'New income tax:', d
return new_error
def test_create_steady_state_parameters():
# Test that SS parameters creates same objects with same inputs.
input_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/create_params_inputs.pkl'))
input_dict['tax_func_type'] = 'DEP'
test_tuple = SS.create_steady_state_parameters(**input_dict)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/create_params_outputs.pkl'))
(income_tax_params, ss_params, iterative_params, chi_params,
small_open_params) = expected_tuple
income_tax_params = ('DEP', ) + income_tax_params
expected_tuple = (income_tax_params, ss_params, iterative_params,
chi_params, small_open_params)
for i, v in enumerate(expected_tuple):
for i2, v2 in enumerate(v):
try:
assert (all(test_tuple[i][i2] == v2))
except ValueError:
assert ((test_tuple[i][i2] == v2).all())
except TypeError:
assert (test_tuple[i][i2] == v2)
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=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,
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 test_euler_equation_solver():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_inputs.pkl'))
(guesses, params) = input_tuple
p = Specifications()
(r, w, T_H, factor, j, p.J, p.S, p.beta, p.sigma, p.ltilde, p.g_y,
p.g_n_ss, tau_payroll, retire, p.mean_income_data, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, j, p.chi_b,
p.chi_n, tau_bq, p.rho, lambdas, p.omega_SS, p.e,
p.analytical_mtrs, etr_params, mtrx_params, mtry_params) = params
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.tax_func_type = 'DEP'
p.lambdas = lambdas.reshape(p.J, 1)
b_splus1 = np.array(guesses[:p.S]).reshape(p.S, 1) + 0.005
BQ = aggregates.get_BQ(r, b_splus1, j, p, 'SS', False)
bq = household.get_bq(BQ, j, p, 'SS')
args = (r, w, bq, T_H, factor, j, p)
test_list = SS.euler_equation_solver(guesses, *args)
expected_list = np.array([
-3.62741663e+00, -6.30068841e+00, -6.76592886e+00,
-6.97731223e+00, -7.05777777e+00, -6.57305440e+00,
-7.11553046e+00, -7.30569622e+00, -7.45808107e+00,
-7.89984062e+00, -8.11466111e+00, -8.28230086e+00,
-8.79253862e+00, -8.86994311e+00, -9.31299476e+00,
-9.80834199e+00, -9.97333771e+00, -1.08349979e+01,
-1.13199826e+01, -1.22890930e+01, -1.31550471e+01,
-1.42753713e+01, -1.55721098e+01, -1.73811490e+01,
-1.88856303e+01, -2.09570569e+01, -2.30559500e+01,
-2.52127149e+01, -2.76119605e+01, -3.03141128e+01,
-3.30900203e+01, -3.62799730e+01, -3.91169706e+01,
-4.24246421e+01, -4.55740527e+01, -4.92914871e+01,
-5.30682805e+01, -5.70043846e+01, -6.06075991e+01,
-6.45251018e+01, -6.86128365e+01, -7.35896515e+01,
-7.92634608e+01, -8.34733231e+01, -9.29802390e+01,
-1.01179788e+02, -1.10437881e+02, -1.20569527e+02,
-1.31569973e+02, -1.43633399e+02, -1.57534056e+02,
-1.73244610e+02, -1.90066728e+02, -2.07980863e+02,
-2.27589046e+02, -2.50241670e+02, -2.76314755e+02,
-3.04930986e+02, -3.36196973e+02, -3.70907934e+02,
-4.10966644e+02, -4.56684022e+02, -5.06945218e+02,
-5.61838645e+02, -6.22617808e+02, -6.90840503e+02,
-7.67825713e+02, -8.54436805e+02, -9.51106365e+02,
-1.05780305e+03, -1.17435473e+03, -1.30045062e+03,
-1.43571221e+03, -1.57971603e+03, -1.73204264e+03,
-1.88430524e+03, -2.03403679e+03, -2.17861987e+03,
-2.31532884e+03, -8.00654731e+03, -5.21487172e-02,
-2.80234170e-01, 4.93894552e-01, 3.11884938e-01, 6.55799607e-01,
5.62182419e-01, 3.86074983e-01, 3.43741491e-01, 4.22461089e-01,
3.63707951e-01, 4.93150010e-01, 4.72813688e-01, 4.07390308e-01,
4.94974186e-01, 4.69900128e-01, 4.37562389e-01, 5.67370182e-01,
4.88965362e-01, 6.40728461e-01, 6.14619979e-01, 4.97173823e-01,
6.19549666e-01, 6.51193557e-01, 4.48906118e-01, 7.93091492e-01,
6.51249363e-01, 6.56307713e-01, 1.12948552e+00, 9.50018058e-01,
6.79613030e-01, 9.51359123e-01, 6.31059147e-01, 7.97896887e-01,
8.44620817e-01, 7.43683837e-01, 1.56693187e+00, 2.75630011e-01,
5.32956891e-01, 1.57110727e+00, 1.22674610e+00, 4.63932928e-01,
1.47225464e+00, 1.16948107e+00, 1.07965795e+00, -3.20557791e-01,
-1.17064127e+00, -7.84880649e-01, -7.60851182e-01, -1.61415945e+00,
-8.30363975e-01, -1.68459409e+00, -1.49260581e+00, -1.84257084e+00,
-1.72143079e+00, -1.43131579e+00, -1.63719219e+00, -1.43874851e+00,
-1.57207905e+00, -1.72909159e+00, -1.98778122e+00, -1.80843826e+00,
-2.12828312e+00, -2.24768762e+00, -2.36961877e+00, -2.49117258e+00,
-2.59914065e+00, -2.82309085e+00, -2.93613362e+00, -3.34446991e+00,
-3.45445086e+00, -3.74962140e+00, -3.78113417e+00, -4.55643800e+00,
-4.86929016e+00, -5.08657898e+00, -5.22054177e+00, -5.54606515e+00,
-5.78478304e+00, -5.93652041e+00, -6.11519786e+00])
assert(np.allclose(np.array(test_list), np.array(expected_list)))
def test_sstpi():
import tempfile
import pickle
import numpy as np
import numpy as np
import cPickle as pickle
import os
import ogusa
ogusa.parameters.DATASET = "REAL"
from ogusa.utils import comp_array
from ogusa.utils import comp_scalar
from ogusa.utils import dict_compare
from ogusa.utils import pickle_file_compare
import ogusa.SS
import ogusa.TPI
from ogusa import parameters, wealth, labor, demographics, income, SS, TPI
globals().update(ogusa.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"
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=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,
test=False,
time_path=True,
baseline=True,
reform={},
user_params={},
guid='',
run_micro=True,
data=None,
client=None,
num_workers=1):
tick = time.time()
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError:
pass
print('In runner, baseline is ', baseline)
# Get parameter class
# Note - set run_micro false when initially load class
# Update later with call to spec.get_tax_function_parameters()
spec = Specifications(run_micro=False,
output_base=output_base,
baseline_dir=baseline_dir,
test=test,
time_path=time_path,
baseline=baseline,
reform=reform,
guid=guid,
data=data,
client=client,
num_workers=num_workers)
spec.update_specifications(user_params)
print('path for tax functions: ', spec.output_base)
spec.get_tax_function_parameters(client, run_micro)
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
ss_outputs = SS.run_SS(spec, client=client)
'''
------------------------------------------------------------------------
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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
pickle.dump(spec, open(param_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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(output_base, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
tpi_output = TPI.run_TPI(spec, client=client)
'''
------------------------------------------------------------------------
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"))
print("Time path iteration complete.")
print("It took {0} seconds to get that part done.".format(time.time() -
tick))
'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:
sim_params[key] = globals()[key]
#pickle.dump(dictionary, open("OUTPUT/Saved_moments/params_given.pkl", "w"))
#call(['python', 'SS.py'])
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)
'''
------------------------------------------------------------------------
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
def test_run_SS(input_path, expected_path):
# Test SS.run_SS function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', input_path))
(income_tax_params, ss_params, iterative_params, chi_params,
small_open_params, baseline, baseline_spending, baseline_dir) =\
input_tuple
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.baseline_dir = baseline_dir
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.maxiter, p.mindist_SS = iterative_params
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
test_dict = SS.run_SS(p, None)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', expected_path))
# delete values key-value pairs that are not in both dicts
del expected_dict['bssmat'], expected_dict['chi_n'], expected_dict['chi_b']
del test_dict['etr_ss'], test_dict['mtrx_ss'], test_dict['mtry_ss']
test_dict['IITpayroll_revenue'] = (test_dict['total_revenue_ss'] -
test_dict['business_revenue'])
del test_dict['T_Pss'], test_dict['T_BQss'], test_dict['T_Wss']
del test_dict['resource_constraint_error'], test_dict['T_Css']
del test_dict['r_gov_ss'], test_dict['r_hh_ss']
test_dict['revenue_ss'] = test_dict.pop('total_revenue_ss')
for k, v in expected_dict.items():
assert(np.allclose(test_dict[k], v))
def runner(output_base, baseline_dir, test=False, time_path=True,
baseline=True, reform={}, user_params={}, guid='',
run_micro=True, data=None, client=None, num_workers=1):
tick = time.time()
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError:
pass
print('In runner, baseline is ', baseline)
# Get parameter class
# Note - set run_micro false when initially load class
# Update later with call to spec.get_tax_function_parameters()
spec = Specifications(run_micro=False, output_base=output_base,
baseline_dir=baseline_dir, test=test,
time_path=time_path, baseline=baseline,
reform=reform, guid=guid, data=data,
client=client, num_workers=num_workers)
spec.update_specifications(user_params)
print('path for tax functions: ', spec.output_base)
spec.get_tax_function_parameters(client, run_micro)
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
ss_outputs = SS.run_SS(spec, client=client)
'''
------------------------------------------------------------------------
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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
pickle.dump(spec, open(param_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"))
# Save pickle with parameter values for the run
param_dir = os.path.join(output_base, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
tpi_output = TPI.run_TPI(spec, client=client)
'''
------------------------------------------------------------------------
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"))
print("Time path iteration complete.")
print("It took {0} seconds to get that part done.".format(
time.time() - tick))
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)
'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:
sim_params[key] = globals()[key]
#pickle.dump(dictionary, open("OUTPUT/Saved_moments/params_given.pkl", "w"))
#call(['python', 'SS.py'])
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)
'''
------------------------------------------------------------------------
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
def test_SS_solver():
# Test SS.SS_solver function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_solver_inputs.pkl'))
(b_guess_init, n_guess_init, rss, T_Hss, factor_ss, Yss, params,
baseline, fsolve_flag, baseline_spending) = input_tuple
(bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, small_open_params) = params
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.maxiter, p.mindist_SS = iterative_params
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/SS_solver_outputs.pkl'))
BQss = expected_dict['BQss']
test_dict = SS.SS_solver(b_guess_init, n_guess_init, rss, BQss, T_Hss,
factor_ss, Yss, p, None, fsolve_flag)
# delete values key-value pairs that are not in both dicts
del expected_dict['bssmat'], expected_dict['chi_n'], expected_dict['chi_b']
del expected_dict['Iss_total']
del test_dict['etr_ss'], test_dict['mtrx_ss'], test_dict['mtry_ss']
test_dict['IITpayroll_revenue'] = (test_dict['total_revenue_ss'] -
test_dict['business_revenue'])
del test_dict['T_Pss'], test_dict['T_BQss'], test_dict['T_Wss']
del test_dict['K_d_ss'], test_dict['K_f_ss'], test_dict['D_d_ss']
del test_dict['D_f_ss'], test_dict['I_d_ss']
del test_dict['debt_service_f'], test_dict['new_borrowing_f']
del test_dict['bqssmat'], test_dict['T_Css'], test_dict['Iss_total']
test_dict['revenue_ss'] = test_dict.pop('total_revenue_ss')
for k, v in expected_dict.items():
print('Testing ', k)
assert(np.allclose(test_dict[k], v))
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 test_euler_equation_solver():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_inputs.pkl'))
(guesses, params) = input_tuple
p = Specifications()
(r, w, T_H, factor, j, p.J, p.S, p.beta, p.sigma, p.ltilde, p.g_y,
p.g_n_ss, tau_payroll, retire, p.mean_income_data, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, j, p.chi_b,
p.chi_n, tau_bq, p.rho, lambdas, p.omega_SS, p.e,
p.analytical_mtrs, etr_params, mtrx_params, mtry_params) = params
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.tax_func_type = 'DEP'
p.lambdas = lambdas.reshape(p.J, 1)
b_splus1 = np.array(guesses[:p.S]).reshape(p.S, 1) + 0.005
BQ = aggregates.get_BQ(r, b_splus1, j, p, 'SS', False)
bq = household.get_bq(BQ, j, p, 'SS')
args = (r, w, bq, T_H, factor, j, p)
test_list = SS.euler_equation_solver(guesses, *args)
expected_list = np.array([
-3.62741663e+00, -6.30068841e+00, -6.76592886e+00,
-6.97731223e+00, -7.05777777e+00, -6.57305440e+00,
-7.11553046e+00, -7.30569622e+00, -7.45808107e+00,
-7.89984062e+00, -8.11466111e+00, -8.28230086e+00,
-8.79253862e+00, -8.86994311e+00, -9.31299476e+00,
-9.80834199e+00, -9.97333771e+00, -1.08349979e+01,
-1.13199826e+01, -1.22890930e+01, -1.31550471e+01,
-1.42753713e+01, -1.55721098e+01, -1.73811490e+01,
-1.88856303e+01, -2.09570569e+01, -2.30559500e+01,
-2.52127149e+01, -2.76119605e+01, -3.03141128e+01,
-3.30900203e+01, -3.62799730e+01, -3.91169706e+01,
-4.24246421e+01, -4.55740527e+01, -4.92914871e+01,
-5.30682805e+01, -5.70043846e+01, -6.06075991e+01,
-6.45251018e+01, -6.86128365e+01, -7.35896515e+01,
-7.92634608e+01, -8.34733231e+01, -9.29802390e+01,
-1.01179788e+02, -1.10437881e+02, -1.20569527e+02,
-1.31569973e+02, -1.43633399e+02, -1.57534056e+02,
-1.73244610e+02, -1.90066728e+02, -2.07980863e+02,
-2.27589046e+02, -2.50241670e+02, -2.76314755e+02,
-3.04930986e+02, -3.36196973e+02, -3.70907934e+02,
-4.10966644e+02, -4.56684022e+02, -5.06945218e+02,
-5.61838645e+02, -6.22617808e+02, -6.90840503e+02,
-7.67825713e+02, -8.54436805e+02, -9.51106365e+02,
-1.05780305e+03, -1.17435473e+03, -1.30045062e+03,
-1.43571221e+03, -1.57971603e+03, -1.73204264e+03,
-1.88430524e+03, -2.03403679e+03, -2.17861987e+03,
-2.31532884e+03, -8.00654731e+03, -5.21487172e-02,
-2.80234170e-01, 4.93894552e-01, 3.11884938e-01, 6.55799607e-01,
5.62182419e-01, 3.86074983e-01, 3.43741491e-01, 4.22461089e-01,
3.63707951e-01, 4.93150010e-01, 4.72813688e-01, 4.07390308e-01,
4.94974186e-01, 4.69900128e-01, 4.37562389e-01, 5.67370182e-01,
4.88965362e-01, 6.40728461e-01, 6.14619979e-01, 4.97173823e-01,
6.19549666e-01, 6.51193557e-01, 4.48906118e-01, 7.93091492e-01,
6.51249363e-01, 6.56307713e-01, 1.12948552e+00, 9.50018058e-01,
6.79613030e-01, 9.51359123e-01, 6.31059147e-01, 7.97896887e-01,
8.44620817e-01, 7.43683837e-01, 1.56693187e+00, 2.75630011e-01,
5.32956891e-01, 1.57110727e+00, 1.22674610e+00, 4.63932928e-01,
1.47225464e+00, 1.16948107e+00, 1.07965795e+00, -3.20557791e-01,
-1.17064127e+00, -7.84880649e-01, -7.60851182e-01, -1.61415945e+00,
-8.30363975e-01, -1.68459409e+00, -1.49260581e+00, -1.84257084e+00,
-1.72143079e+00, -1.43131579e+00, -1.63719219e+00, -1.43874851e+00,
-1.57207905e+00, -1.72909159e+00, -1.98778122e+00, -1.80843826e+00,
-2.12828312e+00, -2.24768762e+00, -2.36961877e+00, -2.49117258e+00,
-2.59914065e+00, -2.82309085e+00, -2.93613362e+00, -3.34446991e+00,
-3.45445086e+00, -3.74962140e+00, -3.78113417e+00, -4.55643800e+00,
-4.86929016e+00, -5.08657898e+00, -5.22054177e+00, -5.54606515e+00,
-5.78478304e+00, -5.93652041e+00, -6.11519786e+00])
assert(np.allclose(np.array(test_list), np.array(expected_list)))
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 test_run_TPI():
# Test TPI.run_TPI function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/run_TPI_inputs.pkl'))
(income_tax_params, tpi_params, iterative_params, small_open_params,
initial_values, SS_values, fiscal_params, biz_tax_params,
output_dir, baseline_spending) = input_tuple
tpi_params = tpi_params + [True]
initial_values = initial_values + (0.0,)
p = Specifications()
(J, S, T, BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n, tau_b, delta_tau,
tau_payroll, tau_bq, p.rho, p.omega, N_tilde, lambdas,
p.imm_rates, p.e, retire, p.mean_income_data, factor, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, p.chi_b, p.chi_n,
theta, p.baseline) = tpi_params
new_param_values = {
'J': J,
'S': S,
'T': T
}
# update parameters instance with new values for test
p.update_specifications(new_param_values, raise_errors=False)
(J, S, T, BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n, tau_b, delta_tau,
tau_payroll, tau_bq, p.rho, p.omega, N_tilde, lambdas,
p.imm_rates, p.e, retire, p.mean_income_data, factor, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, p.chi_b, p.chi_n,
theta, p.baseline) = tpi_params
p.Z = np.ones(p.T + p.S) * Z
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.small_open, ss_firm_r, ss_hh_r = small_open_params
p.ss_firm_r = np.ones(p.T + p.S) * ss_firm_r
p.ss_hh_r = np.ones(p.T + p.S) * ss_hh_r
p.maxiter, p.mindist_SS, p.mindist_TPI = iterative_params
(p.budget_balance, alpha_T, alpha_G, p.tG1, p.tG2, p.rho_G,
p.debt_ratio_ss) = fiscal_params
p.alpha_T = np.concatenate((alpha_T, np.ones(40) * alpha_T[-1]))
p.alpha_G = np.concatenate((alpha_G, np.ones(40) * alpha_G[-1]))
(tau_b, delta_tau) = biz_tax_params
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params, (1, 0, 2))[:p.T, :, :]
p.mtrx_params = np.transpose(mtrx_params, (1, 0, 2))[:p.T, :, :]
p.mtry_params = np.transpose(mtry_params, (1, 0, 2))[:p.T, :, :]
p.lambdas = lambdas.reshape(p.J, 1)
p.output = output_dir
p.baseline_spending = baseline_spending
p.num_workers = 1
(K0, b_sinit, b_splus1init, factor, initial_b, initial_n,
p.omega_S_preTP, initial_debt, D0) = initial_values
# Need to run SS first to get results
ss_outputs = SS.run_SS(p, None)
if p.baseline:
utils.mkdirs(os.path.join(p.baseline_dir, "SS"))
ss_dir = os.path.join(p.baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(p.output_base, "SS"))
ss_dir = os.path.join(p.output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
test_dict = TPI.run_TPI(p, None)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/run_TPI_outputs.pkl'))
# delete values key-value pairs that are not in both dicts
del test_dict['etr_path'], test_dict['mtrx_path'], test_dict['mtry_path']
del test_dict['bmat_s']
test_dict['b_mat'] = test_dict.pop('bmat_splus1')
test_dict['REVENUE'] = test_dict.pop('total_revenue')
test_dict['IITpayroll_revenue'] = (test_dict['REVENUE'][:160] -
test_dict['business_revenue'])
del test_dict['T_P'], test_dict['T_BQ'], test_dict['T_W']
del test_dict['resource_constraint_error'], test_dict['T_C']
del test_dict['r_gov'], test_dict['r_hh']
for k, v in expected_dict.items():
try:
assert(np.allclose(test_dict[k], v, rtol=1e-04, atol=1e-04))
except ValueError:
assert(np.allclose(test_dict[k], v[:p.T, :, :], rtol=1e-04,
atol=1e-04))