def test_makedirs(tmp_path):
'''
Test of utils.makedirs() function
'''
utils.mkdirs(tmp_path)
assert os.path.exists(tmp_path)
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 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_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))
def plot_income_data(ages,
abil_midp,
abil_pcts,
emat,
output_dir=None,
filesuffix=""):
'''
Plot income profiles from models estimated from data.
Args:
Returns:
'''
'''
This function graphs ability matrix in 3D, 2D, log, and nolog
Args:
ages (Numpy array) ages represented in sample, length S
abil_midp (Numpy array): midpoints of income percentile bins in
each ability group
abil_pcts (Numpy array): percent of population in each lifetime
income group, length J
emat (Numpy array): effective labor units by age and lifetime
income group, size SxJ
filesuffix (str): suffix to be added to plot files
Returns:
None
'''
J = abil_midp.shape[0]
abil_mesh, age_mesh = np.meshgrid(abil_midp, ages)
cmap1 = matplotlib.cm.get_cmap('summer')
if output_dir:
# Make sure that directory is created
utils.mkdirs(output_dir)
if J == 1:
# Plot of 2D, J=1 in levels
plt.figure()
plt.plot(ages, emat)
filename = "ability_2D_lev" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
# Plot of 2D, J=1 in logs
plt.figure()
plt.plot(ages, np.log(emat))
filename = "ability_2D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
else:
# Plot of 3D, J>1 in levels
fig10 = plt.figure()
ax10 = fig10.gca(projection='3d')
ax10.plot_surface(age_mesh,
abil_mesh,
emat,
rstride=8,
cstride=1,
cmap=cmap1)
ax10.set_xlabel(r'age-$s$')
ax10.set_ylabel(r'ability type -$j$')
ax10.set_zlabel(r'ability $e_{j,s}$')
filename = "ability_3D_lev" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
# Plot of 3D, J>1 in logs
fig11 = plt.figure()
ax11 = fig11.gca(projection='3d')
ax11.plot_surface(age_mesh,
abil_mesh,
np.log(emat),
rstride=8,
cstride=1,
cmap=cmap1)
ax11.set_xlabel(r'age-$s$')
ax11.set_ylabel(r'ability type -$j$')
ax11.set_zlabel(r'log ability $log(e_{j,s})$')
filename = "ability_3D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
if J <= 10: # Restricted because of line and marker types
# Plot of 2D lines from 3D version in logs
ax = plt.subplot(111)
linestyles = np.array([
"-",
"--",
"-.",
":",
])
markers = np.array(["x", "v", "o", "d", ">", "|"])
pct_lb = 0
for j in range(J):
this_label = (
str(int(np.rint(pct_lb))) + " - " +
str(int(np.rint(pct_lb + 100 * abil_pcts[j]))) + "%")
pct_lb += 100 * abil_pcts[j]
if j <= 3:
ax.plot(ages,
np.log(emat[:, j]),
label=this_label,
linestyle=linestyles[j],
color='black')
elif j > 3:
ax.plot(ages,
np.log(emat[:, j]),
label=this_label,
marker=markers[j - 4],
color='black')
ax.axvline(x=80, color='black', linestyle='--')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.set_xlabel(r'age-$s$')
ax.set_ylabel(r'log ability $log(e_{j,s})$')
filename = "ability_2D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
else:
if J <= 10: # Restricted because of line and marker types
# Plot of 2D lines from 3D version in logs
ax = plt.subplot(111)
linestyles = np.array([
"-",
"--",
"-.",
":",
])
markers = np.array(["x", "v", "o", "d", ">", "|"])
pct_lb = 0
for j in range(J):
this_label = (str(int(np.rint(pct_lb))) + " - " +
str(int(np.rint(pct_lb + 100 * abil_pcts[j]))) +
"%")
pct_lb += 100 * abil_pcts[j]
if j <= 3:
ax.plot(ages,
np.log(emat[:, j]),
label=this_label,
linestyle=linestyles[j],
color='black')
elif j > 3:
ax.plot(ages,
np.log(emat[:, j]),
label=this_label,
marker=markers[j - 4],
color='black')
ax.axvline(x=80, color='black', linestyle='--')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.set_xlabel(r'age-$s$')
ax.set_ylabel(r'log ability $log(e_{j,s})$')
return ax
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 run_TPI(p, client=None):
# unpack tuples of parameters
initial_values, SS_values, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
(Kss, Bss, Lss, rss, wss, BQss, T_Hss, total_revenue_ss, bssmat_splus1,
nssmat, Yss, Gss, theta) = SS_values
(T_Hbaseline, Gbaseline) = baseline_values
print('Government spending breakpoints are tG1: ', p.tG1, '; and tG2:',
p.tG2)
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, p.T, p.T)
domain2 = np.tile(domain.reshape(p.T, 1, 1), (1, p.S, p.J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, p.T).reshape(p.T, 1, 1), (1, p.S, p.J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b # np.zeros((p.T + p.S, p.S, p.J))
n_mat = guesses_n # np.zeros((p.T + p.S, p.S, p.J))
ind = np.arange(p.S)
L_init = np.ones((p.T + p.S, )) * Lss
B_init = np.ones((p.T + p.S, )) * Bss
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_K(b_mat[:p.T], p, 'TPI', False)[:p.T - 1]
B_init[0] = B0
if not p.small_open:
if p.budget_balance:
K_init = B_init
else:
K_init = B_init * Kss / Bss
else:
K_init = firm.get_K(L_init, p.firm_r, p, 'TPI')
K = K_init
L = L_init
B = B_init
Y = np.zeros_like(K)
Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
Y[p.T:] = Yss
r = np.zeros_like(Y)
if not p.small_open:
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = rss
else:
r = p.firm_r
# compute w
w = np.zeros_like(r)
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
w[p.T:] = wss
r_gov = fiscal.get_r_gov(r, p)
if p.budget_balance:
r_hh = r
else:
r_hh = aggr.get_r_hh(r, r_gov, K, p.debt_ratio_ss * Y)
if p.small_open:
r_hh = p.hh_r
BQ0 = aggr.get_BQ(r[0], initial_b, None, p, 'SS', True)
if not p.use_zeta:
BQ = np.zeros((p.T + p.S, p.J))
for j in range(p.J):
BQ[:,
j] = (list(np.linspace(BQ0[j], BQss[j], p.T)) + [BQss[j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, BQss, p.T)) + [BQss] * p.S)
BQ = np.array(BQ)
if p.budget_balance:
if np.abs(T_Hss) < 1e-13:
T_Hss2 = 0.0 # sometimes SS is very small but not zero,
# even if taxes are zero, this get's rid of the approximation
# error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(p.T + p.S) * T_Hss2
total_revenue = T_H
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
T_H = p.alpha_T * Y
elif p.baseline_spending:
T_H = T_Hbaseline
T_H_new = p.T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some starting value
if p.budget_balance:
D = 0.0 * Y
else:
D = p.debt_ratio_ss * Y
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
print('analytical mtrs in tpi = ', p.analytical_mtrs)
print('tax function type in tpi = ', p.tax_func_type)
# TPI loop
while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI):
r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p)
if p.budget_balance:
r_hh[:p.T] = r[:p.T]
else:
K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI')
r_hh[:p.T] = aggr.get_r_hh(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T])
if p.small_open:
r_hh[:p.T] = p.hh_r[:p.T]
outer_loop_vars = (r, w, r_hh, BQ, T_H, theta)
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
lazy_values = []
for j in range(p.J):
guesses = (guesses_b[:, :, j], guesses_n[:, :, j])
lazy_values.append(
delayed(inner_loop)(guesses, outer_loop_vars, initial_values,
j, ind, p))
results = compute(*lazy_values,
scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
for j, result in enumerate(results):
euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result
bmat_s = np.zeros((p.T, p.S, p.J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:p.T - 1, :-1, :]
bmat_splus1 = np.zeros((p.T, p.S, p.J))
bmat_splus1[:, :, :] = b_mat[:p.T, :, :]
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_K(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1]
if np.any(B) < 0:
print('B has negative elements. B[0:9]:', B[0:9])
print('B[T-2:T]:', B[p.T - 2, p.T])
etr_params_4D = np.tile(
p.etr_params.reshape(p.T, p.S, 1, p.etr_params.shape[2]),
(1, 1, p.J, 1))
bqmat = household.get_bq(BQ, None, p, 'TPI')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor,
T_H[:p.T], theta, 0, None, False, 'TPI', p.e,
etr_params_4D, p)
r_hh_path = utils.to_timepath_shape(r_hh, p)
wpath = utils.to_timepath_shape(w, p)
c_mat = household.get_cons(r_hh_path[:p.T, :, :], wpath[:p.T, :, :],
bmat_s, bmat_splus1, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], tax_mat, p.e,
p.tau_c[:p.T, :, :], p)
if not p.small_open:
if p.budget_balance:
K[:p.T] = B[:p.T]
else:
if not p.baseline_spending:
Y = T_H / p.alpha_T # maybe unecessary
(total_rev, T_Ipath, T_Ppath, T_BQpath, T_Wpath,
T_Cpath, business_revenue) = aggr.revenue(
r_hh[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], c_mat[:p.T, :, :], Y[:p.T],
L[:p.T], K[:p.T], factor, theta, etr_params_4D, p,
'TPI')
total_revenue = np.array(
list(total_rev) + [total_revenue_ss] * p.S)
# set intial debt value
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Y[0]
dg_fixed_values = (Y, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov, dg_fixed_values, Gbaseline, p)
K[:p.T] = B[:p.T] - Dnew[:p.T]
if np.any(K < 0):
print('K has negative elements. Setting them ' +
'positive to prevent NAN.')
K[:p.T] = np.fmax(K[:p.T], 0.05 * B[:p.T])
else:
K[:p.T] = firm.get_K(L[:p.T], p.firm_r[:p.T], p, 'TPI')
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
if not p.small_open:
rnew = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
else:
rnew = r.copy()
r_gov_new = fiscal.get_r_gov(rnew, p)
if p.budget_balance:
r_hh_new = rnew[:p.T]
else:
r_hh_new = aggr.get_r_hh(rnew, r_gov_new, K[:p.T], Dnew[:p.T])
if p.small_open:
r_hh_new = p.hh_r[:p.T]
# compute w
wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)),
b_mat[:p.T - 1, :, :],
axis=0)
BQnew = aggr.get_BQ(r_hh_new[:p.T], b_mat_shift, None, p, 'TPI', False)
bqmat_new = household.get_bq(BQnew, None, p, 'TPI')
(total_rev, T_Ipath, T_Ppath, T_BQpath,
T_Wpath, T_Cpath, business_revenue) = aggr.revenue(
r_hh_new[:p.T], wnew[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat_new[:p.T, :, :], c_mat[:p.T, :, :], Ynew[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue = np.array(list(total_rev) + [total_revenue_ss] * p.S)
if p.budget_balance:
T_H_new = total_revenue
elif not p.baseline_spending:
T_H_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update T_H, it's fixed
if p.small_open and not p.budget_balance:
# Loop through years to calculate debt and gov't spending.
# This is done earlier when small_open=False.
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Ynew[0]
dg_fixed_values = (Ynew, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov_new, dg_fixed_values, Gbaseline, p)
if p.budget_balance:
Dnew = D
w[:p.T] = wnew[:p.T]
r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu)
BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu)
D = Dnew
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
T_H[:p.T] = utils.convex_combo(T_H_new[:p.T], T_H[:p.T], p.nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu)
print('r diff: ', (rnew[:p.T] - r[:p.T]).max(),
(rnew[:p.T] - r[:p.T]).min())
print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(),
(BQnew[:p.T] - BQ[:p.T]).min())
print('T_H diff: ', (T_H_new[:p.T] - T_H[:p.T]).max(),
(T_H_new[:p.T] - T_H[:p.T]).min())
print('Y diff: ', (Ynew[:p.T] - Y[:p.T]).max(),
(Ynew[:p.T] - Y[:p.T]).min())
if not p.baseline_spending:
if T_H.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(utils.pct_diff_func(T_H_new[:p.T], T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(np.abs(T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print('Iteration:', TPIiter)
print('\tDistance:', TPIdist)
# Compute effective and marginal tax rates for all agents
mtrx_params_4D = np.tile(
p.mtrx_params.reshape(p.T, p.S, 1, p.mtrx_params.shape[2]),
(1, 1, p.J, 1))
mtry_params_4D = np.tile(
p.mtry_params.reshape(p.T, p.S, 1, p.mtry_params.shape[2]),
(1, 1, p.J, 1))
e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1))
mtry_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
True, e_3D, etr_params_4D, mtry_params_4D, p)
mtrx_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
False, e_3D, etr_params_4D, mtrx_params_4D, p)
etr_path = tax.ETR_income(r_hh_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, e_3D, etr_params_4D,
p)
C = aggr.get_C(c_mat, p, 'TPI')
if not p.small_open:
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
rc_error = Y[:p.T] - C[:p.T] - I[:p.T] - G[:p.T]
else:
I = ((1 + np.squeeze(np.hstack(
(p.g_n[1:p.T], p.g_n_ss)))) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
BI = aggr.get_I(bmat_splus1[:p.T], B[1:p.T + 1], B[:p.T], p, 'TPI')
new_borrowing = (D[1:p.T] * (1 + p.g_n[1:p.T]) * np.exp(p.g_y) -
D[:p.T - 1])
rc_error = (Y[:p.T - 1] + new_borrowing -
(C[:p.T - 1] + BI[:p.T - 1] + G[:p.T - 1]) +
(p.hh_r[:p.T - 1] * B[:p.T - 1] -
(p.delta + p.firm_r[:p.T - 1]) * K[:p.T - 1] -
p.hh_r[:p.T - 1] * D[:p.T - 1]))
# Compute total investment (not just domestic)
I_total = ((1 + p.g_n[:p.T]) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
rce_max = np.amax(np.abs(rc_error))
print('Max absolute value resource constraint error:', rce_max)
print('Checking time path for violations of constraints.')
for t in range(p.T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_mat[t], t,
p.ltilde)
eul_savings = euler_errors[:, :p.S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1)
print('Max Euler error, savings: ', eul_savings)
print('Max Euler error labor supply: ', eul_laborleisure)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y[:p.T],
'B': B,
'K': K,
'L': L,
'C': C,
'I': I,
'I_total': I_total,
'BQ': BQ,
'total_revenue': total_revenue,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Ipath,
'T_H': T_H,
'T_P': T_Ppath,
'T_BQ': T_BQpath,
'T_W': T_Wpath,
'T_C': T_Cpath,
'G': G,
'D': D,
'r': r,
'r_gov': r_gov,
'r_hh': r_hh,
'w': w,
'bmat_splus1': bmat_splus1,
'bmat_s': bmat_s[:p.T, :, :],
'n_mat': n_mat[:p.T, :, :],
'c_path': c_mat,
'bq_path': bqmat,
'tax_path': tax_mat,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': rc_error,
'etr_path': etr_path,
'mtrx_path': mtrx_path,
'mtry_path': mtry_path
}
tpi_dir = os.path.join(p.output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
if np.any(G) < 0:
print('Government spending is negative along transition path' +
' to satisfy budget')
if (((TPIiter >= p.maxiter) or (np.absolute(TPIdist) > p.mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found' +
' (TPIdist)')
if ((np.any(np.absolute(rc_error) >= p.mindist_TPI * 10))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(rc_error)')
if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > p.mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(eulers)')
return output
def run_TPI(p, client=None):
'''
Solve for transition path equilibrium of OG-USA.
Args:
p (OG-USA Specifications object): model parameters
client (Dask client object): client
Returns:
output (dictionary): dictionary with transition path solution
results
'''
# unpack tuples of parameters
initial_values, ss_vars, theta, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n) =\
initial_values
(TRbaseline, Gbaseline, D0_baseline) = baseline_values
print('Government spending breakpoints are tG1: ', p.tG1, '; and tG2:',
p.tG2)
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
guesses_b = utils.get_initial_path(initial_b, ss_vars['bssmat_splus1'], p,
'ratio')
guesses_n = utils.get_initial_path(initial_n, ss_vars['nssmat'], p,
'ratio')
b_mat = guesses_b
n_mat = guesses_n
ind = np.arange(p.S)
# Get path for aggregate savings and labor supply`
L_init = np.ones((p.T + p.S, )) * ss_vars['Lss']
B_init = np.ones((p.T + p.S, )) * ss_vars['Bss']
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_B(b_mat[:p.T], p, 'TPI', False)[:p.T - 1]
B_init[0] = B0
K_init = B_init * ss_vars['Kss'] / ss_vars['Bss']
K = K_init
K_d = K_init * ss_vars['K_d_ss'] / ss_vars['Kss']
K_f = K_init * ss_vars['K_f_ss'] / ss_vars['Kss']
L = L_init
B = B_init
Y = np.zeros_like(K)
Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
Y[p.T:] = ss_vars['Yss']
r = np.zeros_like(Y)
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = ss_vars['rss']
# For case where economy is small open econ
r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1]
# Compute other interest rates
r_gov = fiscal.get_r_gov(r, p)
r_hh = aggr.get_r_hh(r, r_gov, K, ss_vars['Dss'])
# compute w
w = np.zeros_like(r)
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
w[p.T:] = ss_vars['wss']
# initial guesses at fiscal vars
if p.budget_balance:
if np.abs(ss_vars['TR_ss']) < 1e-13:
TR_ss2 = 0.0 # sometimes SS is very small but not zero,
# even if taxes are zero, this get's rid of the
# approximation error, which affects the pct changes below
else:
TR_ss2 = ss_vars['TR_ss']
TR = np.ones(p.T + p.S) * TR_ss2
total_tax_revenue = TR - ss_vars['agg_pension_outlays']
G = np.zeros(p.T + p.S)
D = np.zeros(p.T + p.S)
D_d = np.zeros(p.T + p.S)
D_f = np.zeros(p.T + p.S)
else:
if p.baseline_spending:
TR = TRbaseline
G = Gbaseline
G[p.T:] = ss_vars['Gss']
else:
TR = p.alpha_T * Y
G = np.ones(p.T + p.S) * ss_vars['Gss']
D = np.ones(p.T + p.S) * ss_vars['Dss']
D_d = D * ss_vars['D_d_ss'] / ss_vars['Dss']
D_f = D * ss_vars['D_f_ss'] / ss_vars['Dss']
total_tax_revenue = np.ones(p.T + p.S) * ss_vars['total_tax_revenue']
# Initialize bequests
BQ0 = aggr.get_BQ(r_hh[0], initial_b, None, p, 'SS', True)
if not p.use_zeta:
BQ = np.zeros((p.T + p.S, p.J))
for j in range(p.J):
BQ[:, j] = (list(np.linspace(BQ0[j], ss_vars['BQss'][j], p.T)) +
[ss_vars['BQss'][j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, ss_vars['BQss'], p.T)) +
[ss_vars['BQss']] * p.S)
BQ = np.array(BQ)
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
# TPI loop
while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI):
r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p)
if not p.budget_balance:
K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI')
r_hh[:p.T] = aggr.get_r_hh(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T])
outer_loop_vars = (r, w, r_hh, BQ, TR, theta)
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
lazy_values = []
for j in range(p.J):
guesses = (guesses_b[:, :, j], guesses_n[:, :, j])
lazy_values.append(
delayed(inner_loop)(guesses, outer_loop_vars, initial_values,
j, ind, p))
if client:
futures = client.compute(lazy_values, num_workers=p.num_workers)
results = client.gather(futures)
else:
results = results = compute(*lazy_values,
scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
for j, result in enumerate(results):
euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result
bmat_s = np.zeros((p.T, p.S, p.J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:p.T - 1, :-1, :]
bmat_splus1 = np.zeros((p.T, p.S, p.J))
bmat_splus1[:, :, :] = b_mat[:p.T, :, :]
etr_params_4D = np.tile(
p.etr_params.reshape(p.T, p.S, 1, p.etr_params.shape[2]),
(1, 1, p.J, 1))
bqmat = household.get_bq(BQ, None, p, 'TPI')
trmat = household.get_tr(TR, None, p, 'TPI')
tax_mat = tax.net_taxes(r_hh[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], factor, trmat[:p.T, :, :],
theta, 0, None, False, 'TPI', p.e,
etr_params_4D, p)
r_hh_path = utils.to_timepath_shape(r_hh)
wpath = utils.to_timepath_shape(w)
c_mat = household.get_cons(r_hh_path[:p.T, :, :], wpath[:p.T, :, :],
bmat_s, bmat_splus1, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], tax_mat, p.e,
p.tau_c[:p.T, :, :], p)
y_before_tax_mat = household.get_y(r_hh_path[:p.T, :, :],
wpath[:p.T, :, :],
bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], p)
(total_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays,
bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue,
business_tax_revenue, payroll_tax_revenue,
iit_revenue) = aggr.revenue(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :],
c_mat[:p.T, :, :], Y[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p,
'TPI')
total_tax_revenue[:p.T] = total_tax_rev
dg_fixed_values = (Y, total_tax_revenue, agg_pension_outlays, TR,
Gbaseline, D0_baseline)
(Dnew, G[:p.T], D_d[:p.T], D_f[:p.T], new_borrowing,
debt_service, new_borrowing_f) =\
fiscal.D_G_path(r_gov, dg_fixed_values, p)
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_B(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1]
K_demand_open = firm.get_K(L[:p.T], p.world_int_rate[:p.T], p, 'TPI')
K[:p.T], K_d[:p.T], K_f[:p.T] = aggr.get_K_splits(
B[:p.T], K_demand_open, D_d[:p.T], p.zeta_K[:p.T])
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
rnew = r.copy()
rnew[:p.T] = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
# For case where economy is small open econ
r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1]
r_gov_new = fiscal.get_r_gov(rnew, p)
r_hh_new = aggr.get_r_hh(rnew[:p.T], r_gov_new[:p.T], K[:p.T],
Dnew[:p.T])
# compute w
wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)),
b_mat[:p.T - 1, :, :],
axis=0)
BQnew = aggr.get_BQ(r_hh_new[:p.T], b_mat_shift, None, p, 'TPI', False)
bqmat_new = household.get_bq(BQnew, None, p, 'TPI')
(total_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays,
bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue,
business_tax_revenue, payroll_tax_revenue,
iit_revenue) = aggr.revenue(r_hh_new[:p.T], wnew[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat_new[:p.T, :, :],
c_mat[:p.T, :, :], Ynew[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p,
'TPI')
total_tax_revenue[:p.T] = total_tax_rev
TR_new = fiscal.get_TR(Ynew[:p.T], TR[:p.T], G[:p.T],
total_tax_revenue[:p.T],
agg_pension_outlays[:p.T], p, 'TPI')
# update vars for next iteration
w[:p.T] = wnew[:p.T]
r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu)
BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu)
D[:p.T] = Dnew[:p.T]
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
TR[:p.T] = utils.convex_combo(TR_new[:p.T], TR[:p.T], p.nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu)
print('r diff: ', (rnew[:p.T] - r[:p.T]).max(),
(rnew[:p.T] - r[:p.T]).min())
print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(),
(BQnew[:p.T] - BQ[:p.T]).min())
print('TR diff: ', (TR_new[:p.T] - TR[:p.T]).max(),
(TR_new[:p.T] - TR[:p.T]).min())
print('Y diff: ', (Ynew[:p.T] - Y[:p.T]).max(),
(Ynew[:p.T] - Y[:p.T]).min())
if not p.baseline_spending:
if TR.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(TR_new[:p.T], TR[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(np.abs(TR[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print('Iteration:', TPIiter)
print('\tDistance:', TPIdist)
# Compute effective and marginal tax rates for all agents
mtrx_params_4D = np.tile(
p.mtrx_params.reshape(p.T, p.S, 1, p.mtrx_params.shape[2]),
(1, 1, p.J, 1))
mtry_params_4D = np.tile(
p.mtry_params.reshape(p.T, p.S, 1, p.mtry_params.shape[2]),
(1, 1, p.J, 1))
e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1))
mtry_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
True, e_3D, etr_params_4D, mtry_params_4D, p)
mtrx_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
False, e_3D, etr_params_4D, mtrx_params_4D, p)
etr_path = tax.ETR_income(r_hh_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, e_3D, etr_params_4D,
p)
C = aggr.get_C(c_mat, p, 'TPI')
# Note that implicity in this computation is that immigrants'
# wealth is all in the form of private capital
I_d = aggr.get_I(bmat_splus1[:p.T], K_d[1:p.T + 1], K_d[:p.T], p, 'TPI')
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
# solve resource constraint
# foreign debt service costs
debt_service_f = fiscal.get_debt_service_f(r_hh, D_f)
RC_error = aggr.resource_constraint(Y[:p.T - 1], C[:p.T - 1], G[:p.T - 1],
I_d[:p.T - 1], K_f[:p.T - 1],
new_borrowing_f[:p.T - 1],
debt_service_f[:p.T - 1],
r_hh[:p.T - 1], p)
# Compute total investment (not just domestic)
I_total = aggr.get_I(None, K[1:p.T + 1], K[:p.T], p, 'total_tpi')
# Compute resource constraint error
rce_max = np.amax(np.abs(RC_error))
print('Max absolute value resource constraint error:', rce_max)
print('Checking time path for violations of constraints.')
for t in range(p.T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_mat[t], t,
p.ltilde)
eul_savings = euler_errors[:, :p.S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1)
print('Max Euler error, savings: ', eul_savings)
print('Max Euler error labor supply: ', eul_laborleisure)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y[:p.T],
'B': B,
'K': K,
'K_f': K_f,
'K_d': K_d,
'L': L,
'C': C,
'I': I,
'I_total': I_total,
'I_d': I_d,
'BQ': BQ,
'total_tax_revenue': total_tax_revenue,
'business_tax_revenue': business_tax_revenue,
'iit_payroll_tax_revenue': iit_payroll_tax_revenue,
'iit_revenue': iit_revenue,
'payroll_tax_revenue': payroll_tax_revenue,
'TR': TR,
'agg_pension_outlays': agg_pension_outlays,
'bequest_tax_revenue': bequest_tax_revenue,
'wealth_tax_revenue': wealth_tax_revenue,
'cons_tax_revenue': cons_tax_revenue,
'G': G,
'D': D,
'D_f': D_f,
'D_d': D_d,
'r': r,
'r_gov': r_gov,
'r_hh': r_hh,
'w': w,
'bmat_splus1': bmat_splus1,
'bmat_s': bmat_s[:p.T, :, :],
'n_mat': n_mat[:p.T, :, :],
'c_path': c_mat,
'bq_path': bqmat,
'tr_path': trmat,
'y_before_tax_mat': y_before_tax_mat,
'tax_path': tax_mat,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': RC_error,
'new_borrowing_f': new_borrowing_f,
'debt_service_f': debt_service_f,
'etr_path': etr_path,
'mtrx_path': mtrx_path,
'mtry_path': mtry_path
}
tpi_dir = os.path.join(p.output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
with open(tpi_vars, "wb") as f:
pickle.dump(output, f)
if np.any(G) < 0:
print('Government spending is negative along transition path' +
' to satisfy budget')
if (((TPIiter >= p.maxiter) or (np.absolute(TPIdist) > p.mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found' +
' (TPIdist)')
if ((np.any(np.absolute(RC_error) >= p.mindist_TPI * 10))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(RC_error)')
if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > p.mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(eulers)')
return output
def run_TPI(p, client=None):
# unpack tuples of parameters
initial_values, SS_values, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
(Kss, Bss, Lss, rss, wss, BQss, T_Hss, total_revenue_ss, bssmat_splus1,
nssmat, Yss, Gss, theta) = SS_values
(T_Hbaseline, Gbaseline) = baseline_values
print('Government spending breakpoints are tG1: ', p.tG1,
'; and tG2:', p.tG2)
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, p.T, p.T)
domain2 = np.tile(domain.reshape(p.T, 1, 1), (1, p.S, p.J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, p.T).reshape(p.T, 1, 1), (1, p.S, p.J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b # np.zeros((p.T + p.S, p.S, p.J))
n_mat = guesses_n # np.zeros((p.T + p.S, p.S, p.J))
ind = np.arange(p.S)
L_init = np.ones((p.T + p.S,)) * Lss
B_init = np.ones((p.T + p.S,)) * Bss
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_K(b_mat[:p.T], p, 'TPI', False)[:p.T - 1]
B_init[0] = B0
if not p.small_open:
if p.budget_balance:
K_init = B_init
else:
K_init = B_init * Kss / Bss
else:
K_init = firm.get_K(L_init, p.firm_r, p, 'TPI')
K = K_init
L = L_init
B = B_init
Y = np.zeros_like(K)
Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
Y[p.T:] = Yss
r = np.zeros_like(Y)
if not p.small_open:
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = rss
else:
r = p.firm_r
# compute w
w = np.zeros_like(r)
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
w[p.T:] = wss
r_gov = fiscal.get_r_gov(r, p)
if p.budget_balance:
r_hh = r
else:
r_hh = aggr.get_r_hh(r, r_gov, K, p.debt_ratio_ss * Y)
if p.small_open:
r_hh = p.hh_r
BQ0 = aggr.get_BQ(r[0], initial_b, None, p, 'SS', True)
if not p.use_zeta:
BQ = np.zeros((p.T + p.S, p.J))
for j in range(p.J):
BQ[:, j] = (list(np.linspace(BQ0[j], BQss[j], p.T)) +
[BQss[j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, BQss, p.T)) + [BQss] * p.S)
BQ = np.array(BQ)
if p.budget_balance:
if np.abs(T_Hss) < 1e-13:
T_Hss2 = 0.0 # sometimes SS is very small but not zero,
# even if taxes are zero, this get's rid of the approximation
# error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(p.T + p.S) * T_Hss2
total_revenue = T_H
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
T_H = p.alpha_T * Y
elif p.baseline_spending:
T_H = T_Hbaseline
T_H_new = p.T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some starting value
if p.budget_balance:
D = 0.0 * Y
else:
D = p.debt_ratio_ss * Y
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
print('analytical mtrs in tpi = ', p.analytical_mtrs)
print('tax function type in tpi = ', p.tax_func_type)
# TPI loop
while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI):
r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p)
if p.budget_balance:
r_hh[:p.T] = r[:p.T]
else:
K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI')
r_hh[:p.T] = aggr.get_r_hh(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T])
if p.small_open:
r_hh[:p.T] = p.hh_r[:p.T]
outer_loop_vars = (r, w, r_hh, BQ, T_H, theta)
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
lazy_values = []
for j in range(p.J):
guesses = (guesses_b[:, :, j], guesses_n[:, :, j])
lazy_values.append(
delayed(inner_loop)(guesses, outer_loop_vars,
initial_values, j, ind, p))
results = compute(*lazy_values, scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
for j, result in enumerate(results):
euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result
bmat_s = np.zeros((p.T, p.S, p.J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:p.T-1, :-1, :]
bmat_splus1 = np.zeros((p.T, p.S, p.J))
bmat_splus1[:, :, :] = b_mat[:p.T, :, :]
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_K(bmat_splus1[:p.T], p, 'TPI',
False)[:p.T - 1]
if np.any(B) < 0:
print('B has negative elements. B[0:9]:', B[0:9])
print('B[T-2:T]:', B[p.T - 2, p.T])
etr_params_4D = np.tile(
p.etr_params.reshape(p.T, p.S, 1, p.etr_params.shape[2]),
(1, 1, p.J, 1))
bqmat = household.get_bq(BQ, None, p, 'TPI')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :],
factor, T_H[:p.T], theta, 0, None,
False, 'TPI', p.e, etr_params_4D, p)
r_hh_path = utils.to_timepath_shape(r_hh, p)
wpath = utils.to_timepath_shape(w, p)
c_mat = household.get_cons(r_hh_path[:p.T, :, :], wpath[:p.T, :, :],
bmat_s, bmat_splus1,
n_mat[:p.T, :, :], bqmat[:p.T, :, :],
tax_mat, p.e, p.tau_c[:p.T, :, :], p)
if not p.small_open:
if p.budget_balance:
K[:p.T] = B[:p.T]
else:
if not p.baseline_spending:
Y = T_H / p.alpha_T # maybe unecessary
(total_rev, T_Ipath, T_Ppath, T_BQpath, T_Wpath,
T_Cpath, business_revenue) = aggr.revenue(
r_hh[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], c_mat[:p.T, :, :], Y[:p.T],
L[:p.T], K[:p.T], factor, theta, etr_params_4D,
p, 'TPI')
total_revenue = np.array(list(total_rev) +
[total_revenue_ss] * p.S)
# set intial debt value
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Y[0]
dg_fixed_values = (Y, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov, dg_fixed_values,
Gbaseline, p)
K[:p.T] = B[:p.T] - Dnew[:p.T]
if np.any(K < 0):
print('K has negative elements. Setting them ' +
'positive to prevent NAN.')
K[:p.T] = np.fmax(K[:p.T], 0.05 * B[:p.T])
else:
K[:p.T] = firm.get_K(L[:p.T], p.firm_r[:p.T], p, 'TPI')
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
if not p.small_open:
rnew = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
else:
rnew = r.copy()
r_gov_new = fiscal.get_r_gov(rnew, p)
if p.budget_balance:
r_hh_new = rnew[:p.T]
else:
r_hh_new = aggr.get_r_hh(rnew, r_gov_new, K[:p.T],
Dnew[:p.T])
if p.small_open:
r_hh_new = p.hh_r[:p.T]
# compute w
wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)),
b_mat[:p.T - 1, :, :], axis=0)
BQnew = aggr.get_BQ(r_hh_new[:p.T], b_mat_shift, None, p,
'TPI', False)
bqmat_new = household.get_bq(BQnew, None, p, 'TPI')
(total_rev, T_Ipath, T_Ppath, T_BQpath, T_Wpath, T_Cpath,
business_revenue) = aggr.revenue(
r_hh_new[:p.T], wnew[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat_new[:p.T, :, :], c_mat[:p.T, :, :], Ynew[:p.T],
L[:p.T], K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue = np.array(list(total_rev) +
[total_revenue_ss] * p.S)
if p.budget_balance:
T_H_new = total_revenue
elif not p.baseline_spending:
T_H_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update T_H, it's fixed
if p.small_open and not p.budget_balance:
# Loop through years to calculate debt and gov't spending.
# This is done earlier when small_open=False.
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Ynew[0]
dg_fixed_values = (Ynew, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov_new, dg_fixed_values, Gbaseline, p)
if p.budget_balance:
Dnew = D
w[:p.T] = wnew[:p.T]
r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu)
BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu)
D = Dnew
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
T_H[:p.T] = utils.convex_combo(T_H_new[:p.T], T_H[:p.T], p.nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu)
print('r diff: ', (rnew[:p.T] - r[:p.T]).max(),
(rnew[:p.T] - r[:p.T]).min())
print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(),
(BQnew[:p.T] - BQ[:p.T]).min())
print('T_H diff: ', (T_H_new[:p.T]-T_H[:p.T]).max(),
(T_H_new[:p.T] - T_H[:p.T]).min())
print('Y diff: ', (Ynew[:p.T]-Y[:p.T]).max(),
(Ynew[:p.T] - Y[:p.T]).min())
if not p.baseline_spending:
if T_H.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T],
BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(utils.pct_diff_func(T_H_new[:p.T],
T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T],
BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(np.abs(T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten())
+ list(utils.pct_diff_func(wnew[:p.T], w[:p.T])) +
list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print('Iteration:', TPIiter)
print('\tDistance:', TPIdist)
# Compute effective and marginal tax rates for all agents
mtrx_params_4D = np.tile(
p.mtrx_params.reshape(p.T, p.S, 1, p.mtrx_params.shape[2]),
(1, 1, p.J, 1))
mtry_params_4D = np.tile(
p.mtry_params.reshape(p.T, p.S, 1, p.mtry_params.shape[2]),
(1, 1, p.J, 1))
e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1))
mtry_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, True,
e_3D, etr_params_4D, mtry_params_4D, p)
mtrx_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, False,
e_3D, etr_params_4D, mtrx_params_4D, p)
etr_path = tax.ETR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, e_3D,
etr_params_4D, p)
C = aggr.get_C(c_mat, p, 'TPI')
if not p.small_open:
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
rc_error = Y[:p.T] - C[:p.T] - I[:p.T] - G[:p.T]
else:
I = ((1 + np.squeeze(np.hstack((p.g_n[1:p.T], p.g_n_ss)))) *
np.exp(p.g_y) * K[1:p.T + 1] - (1.0 - p.delta) * K[:p.T])
BI = aggr.get_I(bmat_splus1[:p.T], B[1:p.T + 1], B[:p.T], p, 'TPI')
new_borrowing = (D[1:p.T] * (1 + p.g_n[1:p.T]) *
np.exp(p.g_y) - D[:p.T - 1])
rc_error = (Y[:p.T - 1] + new_borrowing - (
C[:p.T - 1] + BI[:p.T - 1] + G[:p.T - 1]) +
(p.hh_r[:p.T - 1] * B[:p.T - 1] - (
p.delta + p.firm_r[:p.T - 1]) * K[:p.T - 1] -
p.hh_r[:p.T - 1] * D[:p.T - 1]))
# Compute total investment (not just domestic)
I_total = ((1 + p.g_n[:p.T]) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
rce_max = np.amax(np.abs(rc_error))
print('Max absolute value resource constraint error:', rce_max)
print('Checking time path for violations of constraints.')
for t in range(p.T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_mat[t], t, p.ltilde)
eul_savings = euler_errors[:, :p.S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1)
print('Max Euler error, savings: ', eul_savings)
print('Max Euler error labor supply: ', eul_laborleisure)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y[:p.T], 'B': B, 'K': K, 'L': L, 'C': C, 'I': I,
'I_total': I_total, 'BQ': BQ, 'total_revenue': total_revenue,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Ipath, 'T_H': T_H,
'T_P': T_Ppath, 'T_BQ': T_BQpath, 'T_W': T_Wpath,
'T_C': T_Cpath, 'G': G, 'D': D, 'r': r, 'r_gov': r_gov,
'r_hh': r_hh, 'w': w, 'bmat_splus1': bmat_splus1,
'bmat_s': bmat_s[:p.T, :, :], 'n_mat': n_mat[:p.T, :, :],
'c_path': c_mat, 'bq_path': bqmat,
'tax_path': tax_mat, 'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': rc_error,
'etr_path': etr_path, 'mtrx_path': mtrx_path,
'mtry_path': mtry_path}
tpi_dir = os.path.join(p.output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
if np.any(G) < 0:
print('Government spending is negative along transition path' +
' to satisfy budget')
if (((TPIiter >= p.maxiter) or
(np.absolute(TPIdist) > p.mindist_TPI)) and
ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found' +
' (TPIdist)')
if ((np.any(np.absolute(rc_error) >= p.mindist_TPI * 10)) and
ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(rc_error)')
if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > p.mindist_TPI))) and
ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(eulers)')
return output
def runner_SS(output_base,
baseline_dir,
baseline=False,
analytical_mtrs=True,
age_specific=False,
reform=0,
fix_transfers=False,
user_params={},
guid='',
calibrate_model=False,
run_micro=True):
from ogusa import parameters, demographics, income, utils
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
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline,
reform=reform,
guid=guid,
user_modifiable=True)
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 'sigma' in user_params:
print "updating sigma"
run_params['sigma'] = user_params['sigma']
run_params.update(user_params)
from ogusa import SS, TPI, SS_alt
# 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'
]
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
'''
------------------------------------------------------------------------
If using income tax reform, need to determine parameters that yield
same SS revenue as the wealth tax reform.
------------------------------------------------------------------------
'''
if reform == 1:
income_tax_params, ss_params, iterative_params, chi_params = SS.create_steady_state_parameters(
**sim_params)
# find SS revenue from wealth tax reform
reform3_ss_dir = os.path.join(
"./OUTPUT_WEALTH_REFORM" + '/sigma' + str(run_params['sigma']),
"SS/SS_vars.pkl")
reform3_ss_solutions = pickle.load(open(reform3_ss_dir, "rb"))
receipts_to_match = reform3_ss_solutions[
'T_Hss'] + reform3_ss_solutions['Gss']
# create function to match SS revenue
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
print 'Computing new income tax to match wealth tax'
# d_guess= .452 # initial guess 0.452 works for sigma = 2, frisch 1.5
# new_d_inc = d_guess
# import scipy.optimize as opt
# params = [income_tax_params, receipts_to_match, ss_params, iterative_params,
# chi_params, baseline, baseline_dir]
# new_d_inc = opt.fsolve(matcher, d_guess, args=params, xtol=1e-8)
# print '\tOld income tax:', d_guess
# print '\tNew income tax:', new_d_inc
def samesign(a, b):
return a * b > 0
def bisect_method(func, params, low, high):
'Find root of continuous function where f(low) and f(high) have opposite signs'
#assert not samesign(func(params,low), func(params,high))
for i in range(54):
midpoint = (low + high) / 2.0
if samesign(func(params, low), func(params, midpoint)):
low = midpoint
else:
high = midpoint
return midpoint
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
# print 'Computing new income tax to match wealth tax'
# d_guess= 0.5025 # initial guess
# # 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)
# 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']
# error = receipts_to_match - receipts_new
# new_error = error
# print "ERROR: ", error
# max_loop_iter = 1
# output_list = np.zeros((max_loop_iter,3))
# loop_iter = 0
# bisect = 0
# d_guess_old = d_guess
# # while np.abs(new_error) > 1e-8 and loop_iter < max_loop_iter:
# while loop_iter < max_loop_iter:
# # if new_error > 0 and new_error > 0 and bisect == 0:
# # d_guess_old = d_guess
# # d_guess+=0.001
# # elif new_error < 0 and new_error < 0 and bisect == 0:
# # d_guess_old = d_guess
# # d_guess-=0.001
# # d_guess = max(0.0,d_guess) # constrain so not negative
# # else:
# # bisect = 1
# # print 'Entering bisection method'
# # params = income_tax_params, ss_params, iterative_params,\
# # chi_params, baseline ,baseline_dir
# # high = max(d_guess,d_guess_old)
# # low = min(d_guess,d_guess_old)
# # d_guess = bisect_method(solve_model, params, low, high)
# # loop_iter = max_loop_iter
# d_guess_old = d_guess
# d_guess+=0.0005
#
# error = new_error
# 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
# print 'now here$$$'
# 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: ", new_error
# output_list[loop_iter,0]=new_error
# output_list[loop_iter,1]=d_guess
# output_list[loop_iter,2]=ss_outputs['Yss']-ss_outputs['Iss']-ss_outputs['Css']-ss_outputs['Gss']
# np.savetxt('inc_tax_out.csv',output_list, delimiter=",")
# pickle.dump(output_list, open("output_list.pkl", "wb"))
# print 'Error in taxes:', error
# print 'Old income tax:', d_guess_old
# print 'New income tax:', d_guess
# print 'iteration: ', loop_iter
# loop_iter += 1
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
new_d_inc = 0.5025 # this is 0.453 if fix_transfers=False, 0.503 if True
etr_params[:, 3] = new_d_inc
mtrx_params[:, 3] = new_d_inc
mtry_params[:, 3] = new_d_inc
sim_params['etr_params'] = np.tile(
np.reshape(etr_params, (run_params['S'], 1, etr_params.shape[1])),
(1, run_params['BW'], 1))
sim_params['mtrx_params'] = np.tile(
np.reshape(mtrx_params,
(run_params['S'], 1, mtrx_params.shape[1])),
(1, run_params['BW'], 1))
sim_params['mtry_params'] = np.tile(
np.reshape(mtry_params,
(run_params['S'], 1, mtry_params.shape[1])),
(1, run_params['BW'], 1))
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
income_tax_params, ss_params, iterative_params, chi_params = SS.create_steady_state_parameters(
**sim_params)
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
'''
****
CALL CALIBRATION here if boolean flagged
****
'''
if calibrate_model:
chi_params = calibrate.chi_estimate(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline_dir=baseline_dir)
# ss_outputs = SS_alt.run_SS(income_tax_params, ss_params, iterative_params,
# chi_params, baseline, baseline_dir=baseline_dir)
print 'Fix transfers = ', fix_transfers
ss_outputs = SS.run_SS(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline,
fix_transfers=fix_transfers,
baseline_dir=baseline_dir)
model_moments = ogusa.calibrate.calc_moments(ss_outputs,
sim_params['omega_SS'],
sim_params['lambdas'],
sim_params['S'],
sim_params['J'])
scf, data = ogusa.wealth.get_wealth_data()
wealth_moments = ogusa.wealth.compute_wealth_moments(
scf, sim_params['lambdas'], sim_params['J'])
print 'model moments: ', model_moments[:sim_params['J'] + 2]
print 'data moments: ', wealth_moments
'''
------------------------------------------------------------------------
Pickle SS results and parameters
------------------------------------------------------------------------
'''
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"))
param_dir = os.path.join(baseline_dir, "run_parameters.pkl")
pickle.dump(sim_params, 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"))
param_dir = os.path.join(output_base, "run_parameters.pkl")
pickle.dump(sim_params, open(param_dir, "wb"))
def runner(output_base,
baseline_dir,
test=False,
time_path=True,
baseline=True,
iit_reform={},
og_spec={},
guid='',
run_micro=True,
data=None,
client=None,
num_workers=1):
'''
This function runs the OG-USA model, solving for the steady-state
and (optionally) the time path equilibrium.
Args:
output_base (str): path to save output to
baseline_dir (str): path where baseline model results are saved
test (bool): whether to run model in test mode (which has
a smaller state space and higher tolerances for solution)
time_path (bool): whether to solve for the time path equlibrium
baseline (bool): whether the model run is the baseline run
iit_reform (dict): Tax-Calculator policy dictionary
og_spec (dict): dictionary with updates to default
parameters in OG-USA
guid (str): id for OG-USA run
run_micro (bool): whether to estimate tax functions from micro
data or load saved parameters from pickle file
data (str or Pandas DataFrame): path to or data to use in
Tax-Calculator
client (Dask client object): client
num_workers (int): number of workers to use for parallelization
with Dask
Returns:
None
'''
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,
iit_reform=iit_reform,
guid=guid,
data=data,
client=client,
num_workers=num_workers)
spec.update_specifications(og_spec)
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")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
print('JUST SAVED SS output to ', ss_dir)
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
with open(param_dir, "wb") as f:
cloudpickle.dump((spec), f)
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(ss_outputs, f)
# Save pickle with parameter values for the run
param_dir = os.path.join(output_base, "model_params.pkl")
with open(param_dir, "wb") as f:
cloudpickle.dump((spec), f)
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")
with open(tpi_vars, "wb") as f:
pickle.dump(tpi_output, f)
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)
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 run_model(meta_param_dict, adjustment):
'''
Initializes classes from OG-USA that compute the model under
different policies. Then calls function get output objects.
'''
meta_params = MetaParams()
meta_params.adjust(meta_param_dict)
if meta_params.data_source == "PUF":
data = retrieve_puf(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
else:
data = "cps"
# Create output directory structure
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
dirs = [base_dir, reform_dir]
for _dir in dirs:
utils.mkdirs(_dir)
# Dask parmeters
client = None
num_workers = 1
# whether to estimate tax functions from microdata
run_micro = True
# Solve baseline model
base_spec = {
'start_year': meta_param_dict['year'],
'debt_ratio_ss': 2.0,
'r_gov_scale': 1.0,
'r_gov_shift': 0.02,
'zeta_D': [0.4],
'zeta_K': [0.1],
'initial_debt_ratio': 0.78,
'initial_foreign_debt_ratio': 0.4,
'tax_func_type': 'linear',
'age_specific': False
}
base_params = Specifications(run_micro=False,
output_base=base_dir,
baseline_dir=base_dir,
test=False,
time_path=False,
baseline=True,
iit_reform={},
guid='',
data=data,
client=client,
num_workers=num_workers)
base_params.update_specifications(base_spec)
base_params.get_tax_function_parameters(client, run_micro)
base_ss = SS.run_SS(base_params, client=client)
utils.mkdirs(os.path.join(base_dir, "SS"))
ss_dir = os.path.join(base_dir, "SS", "SS_vars.pkl")
with open(ss_dir, "wb") as f:
pickle.dump(base_ss, f)
# Solve reform model
reform_spec = base_spec
reform_spec.update(adjustment['ogusa'])
reform_params = Specifications(run_micro=False,
output_base=reform_dir,
baseline_dir=base_dir,
test=False,
time_path=False,
baseline=False,
iit_reform={},
guid='',
data=data,
client=client,
num_workers=num_workers)
reform_params.update_specifications(reform_spec)
reform_params.get_tax_function_parameters(client, run_micro)
reform_ss = SS.run_SS(reform_params, client=client)
comp_dict = comp_output(base_ss, base_params, reform_ss, reform_params)
return comp_dict
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,
'eta': (np.ones((S, J)) / (S * J))
}
# 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.eta = p.omega.reshape(T + S, S, 1) * lambdas.reshape(1, J)
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.frac_tax_payroll = 0.5 * np.ones(p.T + p.S)
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")
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)
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 expected_dict['I_total']
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['T_H'] = test_dict.pop('TR')
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['y_before_tax_mat'], test_dict['K_f'], test_dict['K_d']
del test_dict['D_d'], test_dict['D_f']
del test_dict['new_borrowing_f'], test_dict['debt_service_f']
del test_dict['iit_revenue'], test_dict['payroll_tax_revenue']
del test_dict['resource_constraint_error'], test_dict['T_C']
del test_dict['r_gov'], test_dict['r_hh'], test_dict['tr_path']
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))
def run_TPI(p, client=None):
# unpack tuples of parameters
initial_values, ss_vars, theta, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
(T_Hbaseline, Gbaseline) = baseline_values
print('Government spending breakpoints are tG1: ', p.tG1, '; and tG2:',
p.tG2)
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, p.T, p.T)
domain2 = np.tile(domain.reshape(p.T, 1, 1), (1, p.S, p.J))
ending_b = ss_vars['bssmat_splus1']
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, p.T).reshape(p.T, 1, 1), (1, p.S, p.J))
guesses_n = domain3 * (ss_vars['nssmat'] - initial_n) + initial_n
ending_n_tail = np.tile(ss_vars['nssmat'].reshape(1, p.S, p.J),
(p.S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b
n_mat = guesses_n
ind = np.arange(p.S)
L_init = np.ones((p.T + p.S, )) * ss_vars['Lss']
B_init = np.ones((p.T + p.S, )) * ss_vars['Bss']
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_K(b_mat[:p.T], p, 'TPI', False)[:p.T - 1]
B_init[0] = B0
if not p.small_open:
if p.budget_balance:
K_init = B_init
else:
K_init = B_init * ss_vars['Kss'] / ss_vars['Bss']
else:
K_init = firm.get_K(L_init, p.firm_r, p, 'TPI')
K = K_init
K_d = K_init * ss_vars['K_d_ss'] / ss_vars['Kss']
K_f = K_init * ss_vars['K_f_ss'] / ss_vars['Kss']
L = L_init
B = B_init
Y = np.zeros_like(K)
Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
Y[p.T:] = ss_vars['Yss']
r = np.zeros_like(Y)
if not p.small_open:
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = ss_vars['rss']
else:
r = p.firm_r
# compute w
w = np.zeros_like(r)
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
w[p.T:] = ss_vars['wss']
r_gov = fiscal.get_r_gov(r, p)
if p.budget_balance:
r_hh = r
else:
r_hh = aggr.get_r_hh(r, r_gov, K, ss_vars['Dss'])
if p.small_open:
r_hh = p.hh_r
BQ0 = aggr.get_BQ(r[0], initial_b, None, p, 'SS', True)
if not p.use_zeta:
BQ = np.zeros((p.T + p.S, p.J))
for j in range(p.J):
BQ[:, j] = (list(np.linspace(BQ0[j], ss_vars['BQss'][j], p.T)) +
[ss_vars['BQss'][j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, ss_vars['BQss'], p.T)) +
[ss_vars['BQss']] * p.S)
BQ = np.array(BQ)
if p.budget_balance:
if np.abs(ss_vars['T_Hss']) < 1e-13:
T_Hss2 = 0.0 # sometimes SS is very small but not zero,
# even if taxes are zero, this get's rid of the approximation
# error, which affects the perc changes below
else:
T_Hss2 = ss_vars['T_Hss']
T_H = np.ones(p.T + p.S) * T_Hss2
total_revenue = T_H
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
T_H = p.alpha_T * Y
G = np.ones(p.T + p.S) * ss_vars['Gss']
elif p.baseline_spending:
T_H = T_Hbaseline
T_H_new = p.T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some starting values
if p.budget_balance:
D = np.zeros(p.T + p.S)
else:
D = np.ones(p.T + p.S) * ss_vars['Dss']
if ss_vars['Dss'] == 0:
D_d = np.zeros(p.T + p.S)
D_f = np.zeros(p.T + p.S)
else:
D_d = D * ss_vars['D_d_ss'] / ss_vars['Dss']
D_f = D * ss_vars['D_f_ss'] / ss_vars['Dss']
total_revenue = np.ones(p.T + p.S) * ss_vars['total_revenue_ss']
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
# TPI loop
while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI):
r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p)
if p.budget_balance:
r_hh[:p.T] = r[:p.T]
else:
K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI')
r_hh[:p.T] = aggr.get_r_hh(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T])
if p.small_open:
r_hh[:p.T] = p.hh_r[:p.T]
outer_loop_vars = (r, w, r_hh, BQ, T_H, theta)
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
lazy_values = []
for j in range(p.J):
guesses = (guesses_b[:, :, j], guesses_n[:, :, j])
lazy_values.append(
delayed(inner_loop)(guesses, outer_loop_vars, initial_values,
j, ind, p))
results = compute(*lazy_values,
scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
for j, result in enumerate(results):
euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result
bmat_s = np.zeros((p.T, p.S, p.J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:p.T - 1, :-1, :]
bmat_splus1 = np.zeros((p.T, p.S, p.J))
bmat_splus1[:, :, :] = b_mat[:p.T, :, :]
etr_params_4D = np.tile(
p.etr_params.reshape(p.T, p.S, 1, p.etr_params.shape[2]),
(1, 1, p.J, 1))
bqmat = household.get_bq(BQ, None, p, 'TPI')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor,
T_H[:p.T], theta, 0, None, False, 'TPI', p.e,
etr_params_4D, p)
r_hh_path = utils.to_timepath_shape(r_hh, p)
wpath = utils.to_timepath_shape(w, p)
c_mat = household.get_cons(r_hh_path[:p.T, :, :], wpath[:p.T, :, :],
bmat_s, bmat_splus1, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], tax_mat, p.e,
p.tau_c[:p.T, :, :], p)
y_before_tax_mat = (r_hh_path[:p.T, :, :] * bmat_s[:p.T, :, :] +
wpath[:p.T, :, :] * p.e * n_mat[:p.T, :, :])
if not p.baseline_spending and not p.budget_balance:
Y[:p.T] = T_H[:p.T] / p.alpha_T[:p.T] # maybe unecessary
(total_rev, T_Ipath, T_Ppath, T_BQpath,
T_Wpath, T_Cpath, business_revenue) = aggr.revenue(
r_hh[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], c_mat[:p.T, :, :], Y[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue[:p.T] = total_rev
# set intial debt value
if p.baseline:
D0 = p.initial_debt_ratio * Y[0]
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Y[0]
dg_fixed_values = (Y, total_revenue, T_H, D0, G_0)
Dnew, G[:p.T] = fiscal.D_G_path(r_gov, dg_fixed_values, Gbaseline,
p)
# Fix initial amount of foreign debt holding
D_f[0] = p.initial_foreign_debt_ratio * Dnew[0]
for t in range(1, p.T):
D_f[t + 1] = (D_f[t] / (np.exp(p.g_y) * (1 + p.g_n[t + 1])) +
p.zeta_D[t] * (Dnew[t + 1] -
(Dnew[t] / (np.exp(p.g_y) *
(1 + p.g_n[t + 1])))))
D_d[:p.T] = Dnew[:p.T] - D_f[:p.T]
else: # if budget balance
Dnew = np.zeros(p.T + 1)
G[:p.T] = np.zeros(p.T)
D_f[:p.T] = np.zeros(p.T)
D_d[:p.T] = np.zeros(p.T)
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_K(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1]
K_demand_open = firm.get_K(L[:p.T], p.firm_r[:p.T], p, 'TPI')
K_d[:p.T] = B[:p.T] - D_d[:p.T]
if np.any(K_d < 0):
print('K_d has negative elements. Setting them ' +
'positive to prevent NAN.')
K_d[:p.T] = np.fmax(K_d[:p.T], 0.05 * B[:p.T])
K_f[:p.T] = p.zeta_K[:p.T] * (K_demand_open - B[:p.T] + D_d[:p.T])
K = K_f + K_d
if np.any(B) < 0:
print('B has negative elements. B[0:9]:', B[0:9])
print('B[T-2:T]:', B[p.T - 2, p.T])
if p.small_open:
K[:p.T] = K_demand_open
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
rnew = r.copy()
if not p.small_open:
rnew[:p.T] = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
else:
rnew[:p.T] = r[:p.T].copy()
r_gov_new = fiscal.get_r_gov(rnew, p)
if p.budget_balance:
r_hh_new = rnew[:p.T]
else:
r_hh_new = aggr.get_r_hh(rnew[:p.T], r_gov_new[:p.T], K[:p.T],
Dnew[:p.T])
if p.small_open:
r_hh_new = p.hh_r[:p.T]
# compute w
wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)),
b_mat[:p.T - 1, :, :],
axis=0)
BQnew = aggr.get_BQ(r_hh_new[:p.T], b_mat_shift, None, p, 'TPI', False)
bqmat_new = household.get_bq(BQnew, None, p, 'TPI')
(total_rev, T_Ipath, T_Ppath, T_BQpath,
T_Wpath, T_Cpath, business_revenue) = aggr.revenue(
r_hh_new[:p.T], wnew[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat_new[:p.T, :, :], c_mat[:p.T, :, :], Ynew[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue[:p.T] = total_rev
if p.budget_balance:
T_H_new = total_revenue
elif not p.baseline_spending:
T_H_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update T_H, it's fixed
# update vars for next iteration
w[:p.T] = wnew[:p.T]
r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu)
BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu)
D[:p.T] = Dnew[:p.T]
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
T_H[:p.T] = utils.convex_combo(T_H_new[:p.T], T_H[:p.T], p.nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu)
print('r diff: ', (rnew[:p.T] - r[:p.T]).max(),
(rnew[:p.T] - r[:p.T]).min())
print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(),
(BQnew[:p.T] - BQ[:p.T]).min())
print('T_H diff: ', (T_H_new[:p.T] - T_H[:p.T]).max(),
(T_H_new[:p.T] - T_H[:p.T]).min())
print('Y diff: ', (Ynew[:p.T] - Y[:p.T]).max(),
(Ynew[:p.T] - Y[:p.T]).min())
if not p.baseline_spending:
if T_H.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(T_H_new[:p.T], T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(np.abs(T_H[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print('Iteration:', TPIiter)
print('\tDistance:', TPIdist)
# Compute effective and marginal tax rates for all agents
mtrx_params_4D = np.tile(
p.mtrx_params.reshape(p.T, p.S, 1, p.mtrx_params.shape[2]),
(1, 1, p.J, 1))
mtry_params_4D = np.tile(
p.mtry_params.reshape(p.T, p.S, 1, p.mtry_params.shape[2]),
(1, 1, p.J, 1))
e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1))
mtry_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
True, e_3D, etr_params_4D, mtry_params_4D, p)
mtrx_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
False, e_3D, etr_params_4D, mtrx_params_4D, p)
etr_path = tax.ETR_income(r_hh_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, e_3D, etr_params_4D,
p)
C = aggr.get_C(c_mat, p, 'TPI')
# Note that implicity in this computation is that immigrants'
# wealth is all in the form of private capital
I_d = aggr.get_I(bmat_splus1[:p.T], K_d[1:p.T + 1], K_d[:p.T], p, 'TPI')
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
# solve resource constraint
# net foreign borrowing
new_borrowing_f = (D_f[1:p.T + 1] * np.exp(p.g_y) *
(1 + p.g_n[1:p.T + 1]) - D_f[:p.T])
debt_service_f = D_f * r_hh
RC_error = aggr.resource_constraint(Y[:p.T - 1], C[:p.T - 1], G[:p.T - 1],
I_d[:p.T - 1], K_f[:p.T - 1],
new_borrowing_f[:p.T - 1],
debt_service_f[:p.T - 1],
r_hh[:p.T - 1], p)
# Compute total investment (not just domestic)
I_total = ((1 + p.g_n[:p.T]) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
rce_max = np.amax(np.abs(RC_error))
print('Max absolute value resource constraint error:', rce_max)
print('Checking time path for violations of constraints.')
for t in range(p.T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_mat[t], t,
p.ltilde)
eul_savings = euler_errors[:, :p.S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1)
print('Max Euler error, savings: ', eul_savings)
print('Max Euler error labor supply: ', eul_laborleisure)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y[:p.T],
'B': B,
'K': K,
'K_f': K_f,
'K_d': K_d,
'L': L,
'C': C,
'I': I,
'I_total': I_total,
'I_d': I_d,
'BQ': BQ,
'total_revenue': total_revenue,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Ipath,
'T_H': T_H,
'T_P': T_Ppath,
'T_BQ': T_BQpath,
'T_W': T_Wpath,
'T_C': T_Cpath,
'G': G,
'D': D,
'D_f': D_f,
'D_d': D_d,
'r': r,
'r_gov': r_gov,
'r_hh': r_hh,
'w': w,
'bmat_splus1': bmat_splus1,
'bmat_s': bmat_s[:p.T, :, :],
'n_mat': n_mat[:p.T, :, :],
'c_path': c_mat,
'bq_path': bqmat,
'y_before_tax_mat': y_before_tax_mat,
'tax_path': tax_mat,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': RC_error,
'new_borrowing_f': new_borrowing_f,
'debt_service_f': debt_service_f,
'etr_path': etr_path,
'mtrx_path': mtrx_path,
'mtry_path': mtry_path
}
tpi_dir = os.path.join(p.output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
if np.any(G) < 0:
print('Government spending is negative along transition path' +
' to satisfy budget')
if (((TPIiter >= p.maxiter) or (np.absolute(TPIdist) > p.mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found' +
' (TPIdist)')
if ((np.any(np.absolute(RC_error) >= p.mindist_TPI * 10))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(RC_error)')
if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > p.mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(eulers)')
return output
def runner_SS(output_base,
baseline_dir,
baseline=False,
analytical_mtrs=True,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True):
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params['start_year'],
reform=reform,
guid=guid)
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
'''
****
CALL CALIBRATION here if boolean flagged
****
'''
calibrate_model = False
# if calibrate_model:
# chi_b, chi_n = calibrate.(income_tax_params, ss_params, iterative_params, chi_params, baseline,
# calibrate_model, output_dir=output_base, baseline_dir=baseline_dir)
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter',
'mindist_SS', 'mindist_TPI', 'analytical_mtrs', 'b_ellipse',
'k_ellipse', 'upsilon', 'chi_b_guess', 'chi_n_guess', 'etr_params',
'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq', 'retire',
'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho'
]
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_params, iterative_params, chi_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_SS(income_tax_params,
ss_params,
iterative_params,
chi_params,
baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
def 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, 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 get_tax_function_parameters(
self,
p,
iit_reform={},
guid="",
data="",
client=None,
num_workers=1,
run_micro=False,
tax_func_path=None,
):
"""
Reads pickle file of tax function parameters or estimates the
parameters from microsimulation model output.
Args:
client (Dask client object): client
run_micro (bool): whether to estimate parameters from
microsimulation model
tax_func_path (string): path where find or save tax
function parameter estimates
Returns:
None
"""
# set paths if none given
if tax_func_path is None:
if p.baseline:
pckl = "TxFuncEst_baseline{}.pkl".format(guid)
tax_func_path = os.path.join(CUR_PATH, pckl)
print("Using baseline tax parameters from ", tax_func_path)
else:
pckl = "TxFuncEst_policy{}.pkl".format(guid)
tax_func_path = os.path.join(CUR_PATH, pckl)
print(
"Using reform policy tax parameters from ", tax_func_path
)
# create directory for tax function pickles to be saved to
mkdirs(os.path.split(tax_func_path)[0])
# If run_micro is false, check to see if parameters file exists
# and if it is consistent with Specifications instance
if not run_micro:
dict_params, run_micro = self.read_tax_func_estimate(tax_func_path)
if run_micro:
txfunc.get_tax_func_estimate( # pragma: no cover
p.BW,
p.S,
p.starting_age,
p.ending_age,
p.baseline,
p.analytical_mtrs,
p.tax_func_type,
p.age_specific,
p.start_year,
iit_reform,
guid,
tax_func_path,
data,
client,
num_workers,
)
dict_params, _ = self.read_tax_func_estimate(p, tax_func_path)
mean_income_data = dict_params["tfunc_avginc"][0]
try:
frac_tax_payroll = np.append(
dict_params["tfunc_frac_tax_payroll"],
np.ones(p.T + p.S - p.BW)
* dict_params["tfunc_frac_tax_payroll"][-1],
)
except KeyError:
pass
try:
taxcalc_version = dict_params["taxcalc_version"]
except KeyError:
taxcalc_version = "No version recorded"
# Reorder indices of tax function and tile for all years after
# budget window ends
num_etr_params = dict_params["tfunc_etr_params_S"].shape[2]
# First check to see if tax parameters that are used were
# estimated with a budget window and ages that are as long as
# the those implied based on the start year and model age.
# N.B. the tax parameters dictionary does not save the years
# that correspond to the parameter estimates, so the start year
# used there may name match what is used in a run that reads in
# some cached tax function parameters. Likewise for age.
params_list = ["etr", "mtrx", "mtry"]
BW_in_tax_params = dict_params["tfunc_etr_params_S"].shape[1]
S_in_tax_params = dict_params["tfunc_etr_params_S"].shape[0]
if p.BW != BW_in_tax_params:
print(
"Warning: There is a discrepency between the start"
+ " year of the model and that of the tax functions!!"
)
# After printing warning, make it work by tiling
if p.BW > BW_in_tax_params:
for item in params_list:
dict_params["tfunc_" + item + "_params_S"] = np.concatenate(
(
dict_params["tfunc_" + item + "_params_S"],
np.tile(
dict_params["tfunc_" + item + "_params_S"][
:, -1, :
].reshape(S_in_tax_params, 1, num_etr_params),
(1, p.BW - BW_in_tax_params, 1),
),
),
axis=1,
)
dict_params["tfunc_avg_" + item] = np.append(
dict_params["tfunc_avg_" + item],
np.tile(
dict_params["tfunc_avg_" + item][-1],
(p.BW - BW_in_tax_params),
),
)
if p.S != S_in_tax_params:
print(
"Warning: There is a discrepency between the ages"
+ " used in the model and those in the tax functions!!"
)
# After printing warning, make it work by tiling
if p.S > S_in_tax_params:
for item in params_list:
dict_params["tfunc_" + item + "_params_S"] = np.concatenate(
(
dict_params["tfunc_" + item + "_params_S"],
np.tile(
dict_params["tfunc_" + item + "_params_S"][
-1, :, :
].reshape(1, p.BW, num_etr_params),
(p.S - S_in_tax_params, 1, 1),
),
),
axis=0,
)
tax_param_dict = {
"etr_params": dict_params["tfunc_etr_params_S"],
"mtrx_params": dict_params["tfunc_mtrx_params_S"],
"mtry_params": dict_params["tfunc_mtry_params_S"],
"taxcalc_version": taxcalc_version,
"mean_income_data": mean_income_data,
"frac_tax_payroll": frac_tax_payroll,
}
return tax_param_dict
def runner(output_base,
baseline_dir,
baseline=False,
analytical_mtrs=True,
age_specific=False,
reform=0,
fix_transfers=False,
user_params={},
guid='',
run_micro=True,
calibrate_model=False):
from ogusa import parameters, demographics, income, utils
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
print("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline,
reform=reform,
guid=guid,
user_modifiable=True)
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 'sigma' in user_params:
print "updating sigma"
run_params['sigma'] = user_params['sigma']
run_params.update(user_params)
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', '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'
]
'''
------------------------------------------------------------------------
If using income tax reform, need to determine parameters that yield
same SS revenue as the wealth tax reform.
------------------------------------------------------------------------
'''
if reform == 1:
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)
# find SS revenue from wealth tax reform
reform3_ss_dir = os.path.join(
"./OUTPUT_WEALTH_REFORM" + '/sigma' + str(run_params['sigma']),
"SS/SS_vars.pkl")
reform3_ss_solutions = pickle.load(open(reform3_ss_dir, "rb"))
receipts_to_match = reform3_ss_solutions['net_tax_receipts']
# create function to match SS revenue
# 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 ,baseline_dir=baseline_dir, output_base=output_base)
#
# 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
# print 'Computing new income tax to match wealth tax'
d_guess = 0.413 # initial guess
# import scipy.optimize as opt
# params = [income_tax_params, receipts_to_match, ss_params, iterative_params,
# chi_params, baseline, baseline_dir]
# new_d_inc = opt.fsolve(matcher, d_guess, args=params, xtol=1e-6)
new_d_inc = 0.413 # this value comes out given default parameter values (if fix_transfers=True this is 0.503 if False then 0.453)
print '\tOld income tax:', d_guess
print '\tNew income tax:', new_d_inc
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
etr_params[:, 3] = new_d_inc
mtrx_params[:, 3] = new_d_inc
mtry_params[:, 3] = new_d_inc
run_params['etr_params'] = np.tile(
np.reshape(etr_params, (run_params['S'], 1, etr_params.shape[1])),
(1, run_params['BW'], 1))
run_params['mtrx_params'] = np.tile(
np.reshape(mtrx_params,
(run_params['S'], 1, mtrx_params.shape[1])),
(1, run_params['BW'], 1))
run_params['mtry_params'] = np.tile(
np.reshape(mtry_params,
(run_params['S'], 1, mtry_params.shape[1])),
(1, run_params['BW'], 1))
'''
------------------------------------------------------------------------
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)
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
print('ETR param shape = ', etr_params.shape)
ss_outputs = SS.run_SS(income_tax_params,
ss_parameters,
iterative_params,
chi_params,
baseline,
fix_transfers=fix_transfers,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results and parameters of run
------------------------------------------------------------------------
'''
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"))
param_dir = os.path.join(baseline_dir, "run_parameters.pkl")
pickle.dump(sim_params, 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"))
param_dir = os.path.join(output_base, "run_parameters.pkl")
pickle.dump(sim_params, open(param_dir, "wb"))
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
sim_params['baseline'] = baseline
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)
tpi_output, macro_output = TPI.run_TPI(income_tax_params,
tpi_params,
iterative_params,
initial_values,
SS_values,
fix_transfers=fix_transfers,
output_dir=output_base)
'''
------------------------------------------------------------------------
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. It"
print "took {0} seconds to get that part done.".format(time.time() - tick)
def runner(output_base,
baseline_dir,
test=False,
time_path=True,
baseline=False,
analytical_mtrs=False,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True,
small_open=False,
budget_balance=False,
baseline_spending=False):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, demographics, income, utils
from ogusa import txfunc
tick = time.time()
# Make sure options are internally consistent
if baseline == True and baseline_spending == True:
print(
'Inconsistent options. Setting <baseline_spending> to False, leaving <baseline> True.'
)
baseline_spending = False
if budget_balance == True and baseline_spending == True:
print(
'Inconsistent options. Setting <baseline_spending> to False, leaving <budget_balance> True.'
)
baseline_spending = False
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [saved_moments_dir, ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline,
analytical_mtrs=analytical_mtrs,
age_specific=age_specific,
start_year=user_params['start_year'],
reform=reform,
guid=guid)
print('In runner, baseline is ', baseline)
run_params = ogusa.parameters.get_parameters(test=test,
baseline=baseline,
guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
run_params['small_open'] = small_open
run_params['budget_balance'] = budget_balance
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print("updating frisch and associated")
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(
user_params['frisch'], run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
if 'debt_ratio_ss' in user_params:
run_params['debt_ratio_ss'] = user_params['debt_ratio_ss']
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print("updating g_y_annual and associated")
ending_age = run_params['ending_age']
starting_age = run_params['starting_age']
S = run_params['S']
g_y = (1 + user_params['g_y_annual'])**(
float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
# Modify transfer & spending ratios based on user input.
if 'T_shifts' in user_params:
if baseline_spending == False:
print('updating ALPHA_T with T_shifts in first',
user_params['T_shifts'].size, 'periods.')
T_shifts = np.concatenate((user_params['T_shifts'],
np.zeros(run_params['ALPHA_T'].size -
user_params['T_shifts'].size)),
axis=0)
run_params['ALPHA_T'] = run_params['ALPHA_T'] + T_shifts
if 'G_shifts' in user_params:
if baseline_spending == False:
print('updating ALPHA_G with G_shifts in first',
user_params['G_shifts'].size, 'periods.')
G_shifts = np.concatenate((user_params['G_shifts'],
np.zeros(run_params['ALPHA_G'].size -
user_params['G_shifts'].size)),
axis=0)
run_params['ALPHA_G'] = run_params['ALPHA_G'] + G_shifts
from ogusa import SS, TPI
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age', 'beta',
'sigma', 'alpha', 'gamma', 'epsilon', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon', 'small_open',
'budget_balance', 'ss_firm_r', 'ss_hh_r', 'tpi_firm_r', 'tpi_hh_r',
'tG1', 'tG2', 'alpha_T', 'alpha_G', 'ALPHA_T', 'ALPHA_G', 'rho_G',
'debt_ratio_ss', 'tau_b', 'delta_tau', 'chi_b_guess', 'chi_n_guess',
'etr_params', 'mtrx_params', 'mtry_params', 'tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth',
'm_wealth', 'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'imm_rates',
'e', 'rho', 'initial_debt', 'omega_S_preTP'
]
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params, small_open_params = SS.create_steady_state_parameters(
**sim_params)
ss_outputs = SS.run_SS(income_tax_params,
ss_parameters,
iterative_params,
chi_params,
small_open_params,
baseline,
baseline_spending,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
sim_params['baseline'] = baseline
sim_params['baseline_spending'] = baseline_spending
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, small_open_params, initial_values, SS_values, fiscal_params, biz_tax_params = TPI.create_tpi_params(
**sim_params)
tpi_output, macro_output = TPI.run_TPI(
income_tax_params,
tpi_params,
iterative_params,
small_open_params,
initial_values,
SS_values,
fiscal_params,
biz_tax_params,
output_dir=output_base,
baseline_spending=baseline_spending)
'''
------------------------------------------------------------------------
Pickle TPI results
------------------------------------------------------------------------
'''
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(tpi_output, open(tpi_vars, "wb"))
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_macro_vars.pkl")
pickle.dump(macro_output, open(tpi_vars, "wb"))
print("Time path iteration complete.")
print("It took {0} seconds to get that part done.".format(time.time() -
tick))
def run_model(meta_param_dict, adjustment):
'''
Initializes classes from OG-USA that compute the model under
different policies. Then calls function get output objects.
'''
print('Meta_param_dict = ', meta_param_dict)
print('adjustment dict = ', adjustment)
meta_params = MetaParams()
meta_params.adjust(meta_param_dict)
if meta_params.data_source == "PUF":
data = retrieve_puf(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
# set name of cached baseline file in case use below
cached_pickle = 'TxFuncEst_baseline_PUF.pkl'
else:
data = "cps"
# set name of cached baseline file in case use below
cached_pickle = 'TxFuncEst_baseline_CPS.pkl'
# Get TC params adjustments
iit_mods = convert_policy_adjustment(
adjustment["Tax-Calculator Parameters"])
# Create output directory structure
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
dirs = [base_dir, reform_dir]
for _dir in dirs:
utils.mkdirs(_dir)
# Dask parmeters
client = Client()
num_workers = 5
# TODO: Swap to these parameters when able to specify tax function
# and model workers separately
# num_workers_txf = 5
# num_workers_mod = 6
# whether to estimate tax functions from microdata
run_micro = True
time_path = meta_param_dict['time_path'][0]['value']
# filter out OG-USA params that will not change between baseline and
# reform runs (these are the non-policy parameters)
filtered_ogusa_params = {}
constant_param_set = {
'frisch', 'beta_annual', 'sigma', 'g_y_annual', 'gamma', 'epsilon',
'Z', 'delta_annual', 'small_open', 'world_int_rate',
'initial_foreign_debt_ratio', 'zeta_D', 'zeta_K', 'tG1', 'tG2',
'rho_G', 'debt_ratio_ss', 'budget_balance'
}
filtered_ogusa_params = OrderedDict()
for k, v in adjustment['OG-USA Parameters'].items():
if k in constant_param_set:
filtered_ogusa_params[k] = v
# Solve baseline model
start_year = meta_param_dict['year'][0]['value']
if start_year == 2020:
OGPATH = inspect.getfile(SS)
OGDIR = os.path.dirname(OGPATH)
tax_func_path = None #os.path.join(OGDIR, 'data', 'tax_functions',
# cached_pickle)
run_micro_baseline = False
else:
tax_func_path = None
run_micro_baseline = True
base_spec = {
**{
'start_year': start_year,
'tax_func_type': 'DEP',
'age_specific': False
},
**filtered_ogusa_params
}
base_params = Specifications(run_micro=False,
output_base=base_dir,
baseline_dir=base_dir,
test=False,
time_path=False,
baseline=True,
iit_reform={},
guid='',
data=data,
client=client,
num_workers=num_workers)
base_params.update_specifications(base_spec)
base_params.get_tax_function_parameters(client,
run_micro_baseline,
tax_func_path=tax_func_path)
base_ss = SS.run_SS(base_params, client=client)
utils.mkdirs(os.path.join(base_dir, "SS"))
base_ss_dir = os.path.join(base_dir, "SS", "SS_vars.pkl")
with open(base_ss_dir, "wb") as f:
pickle.dump(base_ss, f)
if time_path:
base_tpi = TPI.run_TPI(base_params, client=client)
tpi_dir = os.path.join(base_dir, "TPI", "TPI_vars.pkl")
with open(tpi_dir, "wb") as f:
pickle.dump(base_tpi, f)
else:
base_tpi = None
# Solve reform model
reform_spec = base_spec
reform_spec.update(adjustment["OG-USA Parameters"])
reform_params = Specifications(run_micro=False,
output_base=reform_dir,
baseline_dir=base_dir,
test=False,
time_path=time_path,
baseline=False,
iit_reform=iit_mods,
guid='',
data=data,
client=client,
num_workers=num_workers)
reform_params.update_specifications(reform_spec)
reform_params.get_tax_function_parameters(client, run_micro)
reform_ss = SS.run_SS(reform_params, client=client)
utils.mkdirs(os.path.join(reform_dir, "SS"))
reform_ss_dir = os.path.join(reform_dir, "SS", "SS_vars.pkl")
with open(reform_ss_dir, "wb") as f:
pickle.dump(reform_ss, f)
if time_path:
reform_tpi = TPI.run_TPI(reform_params, client=client)
else:
reform_tpi = None
comp_dict = comp_output(base_params, base_ss, reform_params, reform_ss,
time_path, base_tpi, reform_tpi)
# Shut down client and make sure all of its references are
# cleaned up.
client.close()
del client
return comp_dict
def plot_all(base_output_path, reform_output_path, save_path):
'''
Function to plot all default output plots.
Args:
base_output_path (str): path to baseline results
reform_output_path (str): path to reform results
save_path (str): path to save plots to
Returns:
None: All output figures saved to disk.
'''
# Make directory in case it doesn't exist
utils.mkdirs(save_path)
# Read in data
# Read in TPI output and parameters
base_tpi = utils.safe_read_pickle(
os.path.join(base_output_path, 'TPI', 'TPI_vars.pkl')
)
base_ss = utils.safe_read_pickle(
os.path.join(base_output_path, 'SS', 'SS_vars.pkl')
)
base_params = utils.safe_read_pickle(
os.path.join(base_output_path, 'model_params.pkl')
)
reform_tpi = utils.safe_read_pickle(
os.path.join(reform_output_path, 'TPI', 'TPI_vars.pkl')
)
reform_ss = utils.safe_read_pickle(
os.path.join(reform_output_path, 'SS', 'SS_vars.pkl')
)
reform_params = utils.safe_read_pickle(
os.path.join(reform_output_path, 'model_params.pkl')
)
# Percentage changes in macro vars (Y, K, L, C)
plot_aggregates(base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'K', 'L', 'C'], plot_type='pct_diff',
num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Percentage Changes in Macro Aggregates',
path=os.path.join(save_path, 'MacroAgg_PctChange.png'))
# Percentage change in fiscal vars (D, G, TR, Rev)
plot_aggregates(base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['D', 'G', 'TR', 'total_tax_revenue'],
plot_type='pct_diff', num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Percentage Changes in Fiscal Variables',
path=os.path.join(save_path, 'Fiscal_PctChange.png'))
# r and w in baseline and reform -- vertical lines at tG1, tG2
plot_aggregates(base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['r'],
plot_type='levels', num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Real Interest Rates Under Baseline and Reform',
path=os.path.join(save_path, 'InterestRates.png'))
plot_aggregates(base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['w'],
plot_type='levels', num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Wage Rates Under Baseline and Reform',
path=os.path.join(save_path, 'WageRates.png'))
# Debt-GDP in base and reform-- vertical lines at tG1, tG2
plot_gdp_ratio(base_tpi, base_params, reform_tpi, reform_params,
var_list=['D'], num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Debt-to-GDP',
path=os.path.join(save_path, 'DebtGDPratio.png'))
# Tax revenue to GDP in base and reform-- vertical lines at tG1, tG2
plot_gdp_ratio(base_tpi, base_params, reform_tpi, reform_params,
var_list=['total_tax_revenue'], num_years_to_plot=150,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2],
plot_title='Tax Revenue to GDP',
path=os.path.join(save_path, 'RevenueGDPratio.png'))
# Pct change in c, n, b, y, etr, mtrx, mtry by ability group over 10 years
var_list = ['c_path', 'n_mat', 'bmat_splus1', 'etr_path',
'mtrx_path', 'mtry_path', 'y_before_tax_mat']
title_list = ['consumption', 'labor supply', 'savings',
'effective tax rates',
'marginal tax rates on labor income',
'marginal tax rates on capital income',
'before tax income']
path_list = ['Cons', 'Labor', 'Save', 'ETR', 'MTRx', 'MTRy',
'Income']
for i, v in enumerate(var_list):
ability_bar(base_tpi, base_params, reform_tpi, reform_params,
var=v, num_years=10,
start_year=base_params.start_year,
plot_title='Percentage changes in ' + title_list[i],
path=os.path.join(save_path, 'PctChange_' +
path_list[i] + '.png'))
# lifetime profiles, base vs reform, SS for c, n, b, y - not by j
var_list = ['cssmat', 'nssmat', 'bssmat_splus1', 'etr_ss',
'mtrx_ss', 'mtry_ss']
for i, v in enumerate(var_list):
ss_profiles(base_ss, base_params, reform_ss, reform_params,
by_j=False, var=v,
plot_title='Lifecycle Profile of ' + title_list[i],
path=os.path.join(save_path, 'SSLifecycleProfile_' +
path_list[i] + '.png'))
# lifetime profiles, c, n , b, y by j, separately for base and reform
for i, v in enumerate(var_list):
ss_profiles(base_ss, base_params,
by_j=True, var=v,
plot_title='Lifecycle Profile of ' + title_list[i],
path=os.path.join(save_path, 'SSLifecycleProfile_' +
path_list[i] + '_Baseline.png'))
ss_profiles(reform_ss, reform_params,
by_j=True, var=v,
plot_title='Lifecycle Profile of ' + title_list[i],
path=os.path.join(save_path, 'SSLifecycleProfile_' +
path_list[i] + '_Reform.png'))
def runner(output_base, baseline_dir, baseline=False, analytical_mtrs=True, age_specific=False, reform={}, user_params={}, guid='', run_micro=True):
#from ogusa import parameters, wealth, labor, demographics, income
from ogusa import parameters, wealth, labor, demog, income, utils
from ogusa import txfunc
tick = time.time()
#Create output directory structure
saved_moments_dir = os.path.join(output_base, "Saved_moments")
ssinit_dir = os.path.join(output_base, "SSinit")
tpiinit_dir = os.path.join(output_base, "TPIinit")
dirs = [saved_moments_dir, ssinit_dir, tpiinit_dir]
for _dir in dirs:
try:
print "making dir: ", _dir
os.makedirs(_dir)
except OSError as oe:
pass
if run_micro:
txfunc.get_tax_func_estimate(baseline=baseline, analytical_mtrs=analytical_mtrs, age_specific=age_specific,
start_year=user_params['start_year'], reform=reform, guid=guid)
print ("in runner, baseline is ", baseline)
run_params = ogusa.parameters.get_parameters(baseline=baseline, guid=guid)
run_params['analytical_mtrs'] = analytical_mtrs
# Modify ogusa parameters based on user input
if 'frisch' in user_params:
print "updating fricsh and associated"
b_ellipse, upsilon = ogusa.elliptical_u_est.estimation(user_params['frisch'],
run_params['ltilde'])
run_params['b_ellipse'] = b_ellipse
run_params['upsilon'] = upsilon
run_params.update(user_params)
# Modify ogusa parameters based on user input
if 'g_y_annual' in user_params:
print "updating g_y_annual and associated"
g_y = (1 + user_params['g_y_annual'])**(float(ending_age - starting_age) / S) - 1
run_params['g_y'] = g_y
run_params.update(user_params)
from ogusa import SS, TPI
# Generate Wealth data moments
wealth.get_wealth_data(run_params['lambdas'], run_params['J'], run_params['flag_graphs'], output_dir=output_base)
# Generate labor data moments
labor.labor_data_moments(run_params['flag_graphs'], output_dir=output_base)
calibrate_model = False
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'BW', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'analytical_mtrs', 'b_ellipse', 'k_ellipse', 'upsilon',
'chi_b_guess', 'chi_n_guess','etr_params','mtrx_params',
'mtry_params','tau_payroll', 'tau_bq',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho']
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
sim_params = {}
for key in param_names:
sim_params[key] = run_params[key]
sim_params['output_dir'] = output_base
sim_params['run_params'] = run_params
income_tax_params, ss_parameters, iterative_params, chi_params = SS.create_steady_state_parameters(**sim_params)
ss_outputs = SS.run_SS(income_tax_params, ss_parameters, iterative_params, chi_params, baseline,
baseline_dir=baseline_dir)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_dir, "SS"))
ss_dir = os.path.join(output_dir, "SS/ss_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
'''
------------------------------------------------------------------------
Run the baseline TPI simulation
------------------------------------------------------------------------
'''
sim_params['input_dir'] = output_base
sim_params['baseline_dir'] = baseline_dir
income_tax_params, tpi_params, iterative_params, initial_values, SS_values = TPI.create_tpi_params(**sim_params)
# ss_outputs['income_tax_params'] = income_tax_params
# ss_outputs['wealth_tax_params'] = wealth_tax_params
# ss_outputs['ellipse_params'] = ellipse_params
# ss_outputs['parameters'] = parameters
# ss_outputs['N_tilde'] = N_tilde
# ss_outputs['omega_stationary'] = omega_stationary
# ss_outputs['K0'] = K0
# ss_outputs['b_sinit'] = b_sinit
# ss_outputs['b_splus1init'] = b_splus1init
# ss_outputs['L0'] = L0
# ss_outputs['Y0'] = Y0
# ss_outputs['r0'] = r0
# ss_outputs['BQ0'] = BQ0
# ss_outputs['T_H_0'] = T_H_0
# ss_outputs['factor_ss'] = factor
# ss_outputs['tax0'] = tax0
# ss_outputs['c0'] = c0
# ss_outputs['initial_b'] = initial_b
# ss_outputs['initial_n'] = initial_n
# ss_outputs['tau_bq'] = tau_bq
# ss_outputs['g_n_vector'] = g_n_vector
# ss_outputs['output_dir'] = output_base
# with open("ss_outputs.pkl", 'wb') as fp:
# pickle.dump(ss_outputs, fp)
w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_TPI(income_tax_params,
tpi_params, iterative_params, initial_values, SS_values, output_dir=output_base)
print "getting to here...."
TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs)
print "took {0} seconds to get that part done.".format(time.time() - tick)
def runner(output_base,
baseline_dir,
test=False,
time_path=True,
baseline=False,
constant_rates=True,
tax_func_type='DEP',
analytical_mtrs=False,
age_specific=False,
reform={},
user_params={},
guid='',
run_micro=True,
small_open=False,
budget_balance=False,
baseline_spending=False,
data=None,
client=None,
num_workers=1):
from ogusa import parameters, demographics, income, utils
tick = time.time()
start_year = user_params.get('start_year', DEFAULT_START_YEAR)
if start_year > TC_LAST_YEAR:
raise RuntimeError("Start year is beyond data extrapolation.")
# Make sure options are internally consistent
if baseline and baseline_spending:
print("Inconsistent options. Setting <baseline_spending> to False, "
"leaving <baseline> True.'")
baseline_spending = False
if budget_balance and baseline_spending:
print("Inconsistent options. Setting <baseline_spending> to False, "
"leaving <budget_balance> True.")
baseline_spending = False
# 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
print('In runner, baseline is ', baseline)
if small_open and (not isinstance(small_open, dict)):
raise ValueError(
'small_open must be False/None or a dict with keys: {}'.format(
SMALL_OPEN_KEYS))
small_open = small_open or {}
run_params = ogusa.parameters.get_parameters(
output_base,
reform=reform,
test=test,
baseline=baseline,
guid=guid,
run_micro=run_micro,
constant_rates=constant_rates,
analytical_mtrs=analytical_mtrs,
tax_func_type=tax_func_type,
age_specific=age_specific,
start_year=start_year,
data=data,
client=client,
num_workers=num_workers,
**small_open)
run_params['analytical_mtrs'] = analytical_mtrs
run_params['small_open'] = bool(small_open)
run_params['budget_balance'] = budget_balance
run_params['world_int_rate'] = small_open.get('world_int_rate',
DEFAULT_WORLD_INT_RATE)
# 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']
if 'tau_b' in user_params:
run_params['tau_b'] = user_params['tau_b']
# 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 not baseline_spending:
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 not baseline_spending:
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
sim_params['tax_func_type'] = tax_func_type
(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,
client=client,
num_workers=num_workers)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
model_params = {}
for key in param_names:
model_params[key] = sim_params[key]
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(model_params, 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(model_params, open(param_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 = 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,
client=client,
num_workers=num_workers)
'''
------------------------------------------------------------------------
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, 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)
