def read_tax_func_estimate(self, pickle_path, pickle_file):
'''
This function reads in tax function parameters from pickle
files.
Args:
pickle_path (str): path to pickle with tax function
parameter estimates
pickle_file (str): name of pickle file with tax function
parameter estimates
Returns:
dict_params (dict): dictionary containing arrays of tax
function parameters
'''
if os.path.exists(pickle_path):
print('pickle path exists')
dict_params = safe_read_pickle(pickle_path)
else:
path_in_egg = pickle_file
pkl_path = os.path.join(os.path.dirname(__file__), 'tests',
path_in_egg)
dict_params = dict_params = safe_read_pickle(pkl_path)
return dict_params
def test_inner_loop():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'inner_loop_inputs.pkl'))
(outer_loop_vars_in, params, baseline, baseline_spending) = input_tuple
ss_params, income_tax_params, chi_params, small_open_params = params
(bssmat, nssmat, r, Y, TR, factor) = outer_loop_vars_in
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.eta = (p.omega_SS.reshape(p.S, 1) *
p.lambdas.reshape(1, p.J)).reshape(1, p.S, p.J)
p.Z = np.ones(p.T + p.S) * Z
p.zeta_D = np.zeros(p.T + p.S)
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
BQ = np.ones(p.J) * 0.00019646295986015257
outer_loop_vars = (bssmat, nssmat, r, BQ, Y, TR, factor)
(euler_errors, new_bmat, new_nmat, new_r, new_r_gov, new_r_hh,
new_w, new_TR, new_Y, new_factor, new_BQ,
average_income_model) = SS.inner_loop(outer_loop_vars, p, None)
test_tuple = (euler_errors, new_bmat, new_nmat, new_r, new_w,
new_TR, new_Y, new_factor, new_BQ,
average_income_model)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert(np.allclose(test_tuple[i], v, atol=1e-05))
def test_inner_loop():
# Test TPI.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'tpi_inner_loop_inputs.pkl'))
guesses, outer_loop_vars, params, j = input_tuple
income_tax_params, tpi_params, initial_values, ind = params
initial_values = initial_values
tpi_params = tpi_params
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z,
p.delta, p.ltilde, p.nu, p.g_y, p.g_n, 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(p.T + p.S, p.S, 1) * p.lambdas.reshape(1, p.J)
p.Z = np.ones(p.T + p.S) * Z
p.zeta_D = np.zeros(p.T + p.S)
p.initial_foreign_debt_ratio = 0.0
p.initial_debt_ratio = 0.59
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.tax_func_type = 'DEP'
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.num_workers = 1
(K0, b_sinit, b_splus1init, factor, initial_b, initial_n, p.omega_S_preTP,
initial_debt, D0) = initial_values
initial_values_in = (K0, b_sinit, b_splus1init, factor, initial_b,
initial_n, D0)
(r, K, BQ, TR) = outer_loop_vars
wss = firm.get_w_from_r(r[-1], p, 'SS')
w = np.ones(p.T + p.S) * wss
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
outer_loop_vars_in = (r, w, r, BQ, TR, theta)
guesses = (guesses[0], guesses[1])
test_tuple = TPI.inner_loop(guesses, outer_loop_vars_in, initial_values_in,
j, ind, p)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'tpi_inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert (np.allclose(test_tuple[i], v))
def tpi_output(request):
ref_idx = request.param
if ref_idx == "baseline":
return (safe_read_pickle(REG_BASELINE + "/TPI/TPI_vars.pkl"),
safe_read_pickle(BASELINE + "/TPI/TPI_vars.pkl"))
else:
return (safe_read_pickle(
REG_REFORM.format(ref_idx=request.param) + "/TPI/TPI_vars.pkl"),
safe_read_pickle(
REFORM.format(ref_idx=request.param) +
"/TPI/TPI_vars.pkl"))
def parameter_read(request):
ref_idx = request.param
if ref_idx == "baseline":
return (safe_read_pickle(REG_BASELINE + "/model_params.pkl"),
safe_read_pickle(BASELINE + "/model_params.pkl"))
else:
return (safe_read_pickle(
REG_REFORM.format(ref_idx=request.param) + "/model_params.pkl"),
safe_read_pickle(
REFORM.format(ref_idx=request.param) +
"/model_params.pkl"))
def txfunc_output(request):
ref_idx = request.param
if ref_idx == "baseline":
reg_path = REG_BASELINE + "/TxFuncEst_{idx}.pkl".format(idx=ref_idx)
path = BASELINE + "/TxFuncEst_{idx}.pkl".format(idx=ref_idx)
return (safe_read_pickle(reg_path), safe_read_pickle(path))
else:
reg_path = (REG_REFORM.format(ref_idx=ref_idx) +
"/TxFuncEst_policy{idx}.pkl".format(idx=ref_idx))
path = (REFORM.format(ref_idx=ref_idx) +
"/TxFuncEst_policy{idx}.pkl".format(idx=ref_idx))
return (safe_read_pickle(reg_path), safe_read_pickle(path))
def test_firstdoughnutring():
# Test TPI.firstdoughnutring 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', 'firstdoughnutring_inputs.pkl'))
guesses, r, w, b, BQ, TR, j, params = input_tuple
income_tax_params, tpi_params, initial_b = params
tpi_params = tpi_params + [True]
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z,
p.delta, p.ltilde, p.nu, p.g_y, p.g_n, 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.zeta_D = np.zeros(p.T + p.S)
p.initial_foreign_debt_ratio = 0.0
p.initial_debt_ratio = 0.59
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.tax_func_type = 'DEP'
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params, (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params, (1, 0, 2))
p.mtry_params = np.transpose(mtry_params, (1, 0, 2))
p.lambdas = lambdas.reshape(p.J, 1)
p.num_workers = 1
bq = BQ / p.lambdas[j]
tr = TR
test_list = TPI.firstdoughnutring(guesses, r, w, bq, tr, theta, factor, j,
initial_b, p)
expected_list = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data',
'firstdoughnutring_outputs.pkl'))
assert (np.allclose(np.array(test_list), np.array(expected_list)))
def test_twist_doughnut():
# Test TPI.twist_doughnut 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', 'twist_doughnut_inputs.pkl'))
guesses, r, w, BQ, TR, j, s, t, params = input_tuple
income_tax_params, tpi_params, initial_b = params
tpi_params = tpi_params + [True]
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z,
p.delta, p.ltilde, p.nu, p.g_y, p.g_n, 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_c = np.ones((p.T + p.S, p.S, p.J)) * 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.tax_func_type = 'DEP'
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.lambdas = lambdas.reshape(p.J, 1)
p.num_workers = 1
length = int(len(guesses) / 2)
tau_c_to_use = np.diag(p.tau_c[:p.S, :, j], p.S - (s + 2))
bq = BQ[t:t + length] / p.lambdas[j]
tr = TR[t:t + length]
test_list = TPI.twist_doughnut(guesses, r, w, bq, tr, theta, factor, j, s,
t, tau_c_to_use, etr_params, mtrx_params,
mtry_params, initial_b, p)
expected_list = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'twist_doughnut_outputs.pkl'))
assert (np.allclose(np.array(test_list), np.array(expected_list)))
def test_txfunc_est():
# Test txfunc.txfunc_est() function. The test is that given
# inputs from previous run, the outputs are unchanged.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data/txfunc_est_inputs.pkl'))
(df, s, t, rate_type, output_dir, graph) = input_tuple
tax_func_type = 'DEP'
numparams = 12
test_tuple = txfunc.txfunc_est(df, s, t, rate_type, tax_func_type,
numparams, output_dir, graph)
expected_tuple = (np.array([
6.37000261e-22, 2.73404765e-03, 1.62463424e-08, 1.48147213e-02,
2.32797191e-01, -3.69059719e-02, 1.00000000e-04, -1.01967001e-01,
3.96030035e-02, 1.02987671e-01, -1.30433574e-01, 1.00000000e+00
]), 19527.162030047846, 3798)
for i, v in enumerate(expected_tuple):
assert (np.allclose(test_tuple[i], v))
from __future__ import print_function
import pytest
import numpy as np
import os
from ogindia import SS, utils, aggregates, household
from ogindia.parameters import Specifications
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'SS_fsolve_inputs.pkl'))
guesses_in, params = input_tuple
params = params + (None, 1)
(bssmat, nssmat, chi_params, ss_params, income_tax_params,
iterative_params, small_open_params, client, num_workers) = params
p1 = Specifications()
(p1.J, p1.S, p1.T, p1.BW, p1.beta, p1.sigma, p1.alpha, p1.gamma, p1.epsilon,
Z, p1.delta, p1.ltilde, p1.nu, p1.g_y, p1.g_n_ss, tau_payroll,
tau_bq, p1.rho, p1.omega_SS, p1.budget_balance, alpha_T,
p1.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p1.e,
retire, p1.mean_income_data, h_wealth, p_wealth, m_wealth,
p1.b_ellipse, p1.upsilon) = ss_params
p1.eta = (p1.omega_SS.reshape(p1.S, 1) *
p1.lambdas.reshape(1, p1.J)).reshape(1, p1.S, p1.J)
p1.Z = np.ones(p1.T + p1.S) * Z
p1.tau_bq = np.ones(p1.T + p1.S) * 0.0
p1.tau_payroll = np.ones(p1.T + p1.S) * tau_payroll
p1.alpha_T = np.ones(p1.T + p1.S) * alpha_T
p1.tau_b = np.ones(p1.T + p1.S) * tau_b
p1.delta_tau = np.ones(p1.T + p1.S) * delta_tau
p1.h_wealth = np.ones(p1.T + p1.S) * h_wealth
p1.p_wealth = np.ones(p1.T + p1.S) * p_wealth
'''
Tests of parameter_table.py module
'''
import pytest
import os
from ogindia import utils, parameter_tables
# Load in test results and parameters
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
base_params = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'model_params_baseline.pkl'))
base_taxfunctions = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'TxFuncEst_baseline.pkl'))
def test_tax_rate_table():
str = parameter_tables.tax_rate_table(base_taxfunctions, base_params)
assert str
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.omega_SS = p.omega[-1, :]
p.eta = p.omega.reshape(T + S, S, 1) * lambdas.reshape(1, J)
p.Z = np.ones(p.T + p.S) * Z
p.zeta_D = np.zeros(p.T + p.S)
p.initial_foreign_debt_ratio = 0.0
p.initial_debt_ratio = 0.59
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 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['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))
'''
Tests of output_tables.py module
'''
import pytest
import os
import pandas as pd
from ogindia import utils, output_tables
# Load in test results and parameters
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
base_tpi = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'TPI_vars_baseline.pkl'))
base_params = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'model_params_baseline.pkl'))
reform_tpi = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'TPI_vars_reform.pkl'))
reform_params = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'model_params_reform.pkl'))
def test_macro_table():
df = output_tables.macro_table(base_tpi, base_params, reform_tpi,
reform_params)
assert isinstance(df, pd.DataFrame)
def dump_diff_output(baseline_dir, policy_dir):
'''
This function reads the pickles with the SS and time path results
from the baseline and reform and then calculates the percentage
differences between the two for each year in the 10-year budget
window, over the entire budget window, and in the SS.
Args:
baseline_dir (str): path for directory with baseline policy
results
policy_dir (str): path for directory with reform policy results
Returns:
baseline_macros (Numpy array): time path for relevant macro
variables from baseline equilibrium, order of variables
is Y, C, I, L, w, r, total_revenue
policy_macros (Numpy array): time path for relevant macro
variables from reform equilibrium
pct_changes (Numpy array): percentage changes in macro variables
from baseline to reform for each year in the time path
'''
# read macro output
tpi_baseline_dir = os.path.join(baseline_dir, "TPI")
tpi_policy_dir = os.path.join(policy_dir, "TPI")
if not os.path.exists(tpi_policy_dir):
os.mkdir(tpi_policy_dir)
tpi_macro_vars_policy_path = os.path.join(tpi_policy_dir, "TPI_vars.pkl")
tpi_macro_vars_policy = safe_read_pickle(tpi_macro_vars_policy_path)
tpi_macro_vars_baseline_path = os.path.join(tpi_baseline_dir,
"TPI_vars.pkl")
tpi_macro_vars_baseline = safe_read_pickle(tpi_macro_vars_baseline_path)
T = len(tpi_macro_vars_baseline['C'])
baseline_macros = np.zeros((7, T))
baseline_macros[0, :] = tpi_macro_vars_baseline['Y'][:T]
baseline_macros[1, :] = tpi_macro_vars_baseline['C'][:T]
baseline_macros[2, :] = tpi_macro_vars_baseline['I'][:T]
baseline_macros[3, :] = tpi_macro_vars_baseline['L'][:T]
baseline_macros[4, :] = tpi_macro_vars_baseline['w'][:T]
baseline_macros[5, :] = tpi_macro_vars_baseline['r'][:T]
baseline_macros[6, :] = tpi_macro_vars_baseline['total_revenue'][:T]
policy_macros = np.zeros((7, T))
policy_macros[0, :] = tpi_macro_vars_policy['Y'][:T]
policy_macros[1, :] = tpi_macro_vars_policy['C'][:T]
policy_macros[2, :] = tpi_macro_vars_policy['I'][:T]
policy_macros[3, :] = tpi_macro_vars_policy['L'][:T]
policy_macros[4, :] = tpi_macro_vars_policy['w'][:T]
policy_macros[5, :] = tpi_macro_vars_policy['r'][:T]
policy_macros[6, :] = tpi_macro_vars_policy['total_revenue'][:T]
pct_changes = np.zeros((7, 12))
# pct changes for each year in budget window
pct_changes[:, :10] = ((policy_macros - baseline_macros) /
policy_macros)[:, :10]
# pct changes over entire budget window
pct_changes[:, 10] = ((policy_macros[:, :10].sum(axis=1) -
baseline_macros[:, :10].sum(axis=1)) /
policy_macros[:, :10].sum(axis=1))
# Load SS results
ss_policy_path = os.path.join(policy_dir, "SS", "SS_vars.pkl")
ss_policy = safe_read_pickle(ss_policy_path)
ss_baseline_path = os.path.join(baseline_dir, "SS", "SS_vars.pkl")
ss_baseline = safe_read_pickle(ss_baseline_path)
# pct changes in macro aggregates in SS
pct_changes[0, 11] = ((ss_policy['Yss'] - ss_baseline['Yss']) /
ss_baseline['Yss'])
pct_changes[1, 11] = ((ss_policy['Css'] - ss_baseline['Css']) /
ss_baseline['Css'])
pct_changes[2, 11] = ((ss_policy['Iss'] - ss_baseline['Iss']) /
ss_baseline['Iss'])
pct_changes[3, 11] = ((ss_policy['Lss'] - ss_baseline['Lss']) /
ss_baseline['Lss'])
pct_changes[4, 11] = ((ss_policy['wss'] - ss_baseline['wss']) /
ss_baseline['wss'])
pct_changes[5, 11] = ((ss_policy['rss'] - ss_baseline['rss']) /
ss_baseline['rss'])
pct_changes[6, 11] = (
(ss_policy['total_revenue_ss'] - ss_baseline['total_revenue_ss']) /
ss_baseline['total_revenue_ss'])
return pct_changes, baseline_macros, policy_macros
def test_euler_equation_solver():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'euler_eqn_solver_inputs.pkl'))
(guesses, params) = input_tuple
p = Specifications()
(r, w, TR, factor, j, p.J, p.S, p.beta, p.sigma, p.ltilde, p.g_y,
p.g_n_ss, tau_payroll, retire, p.mean_income_data, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, j, p.chi_b,
p.chi_n, tau_bq, p.rho, lambdas, p.omega_SS, p.e,
p.analytical_mtrs, etr_params, mtrx_params, mtry_params) = params
p.eta = (p.omega_SS.reshape(p.S, 1) *
p.lambdas.reshape(1, p.J)).reshape(1, p.S, p.J)
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.tax_func_type = 'DEP'
p.lambdas = lambdas.reshape(p.J, 1)
b_splus1 = np.array(guesses[:p.S]).reshape(p.S, 1) + 0.005
BQ = aggregates.get_BQ(r, b_splus1, j, p, 'SS', False)
bq = household.get_bq(BQ, j, p, 'SS')
tr = household.get_tr(TR, j, p, 'SS')
args = (r, w, bq, tr, factor, j, p)
test_list = SS.euler_equation_solver(guesses, *args)
expected_list = np.array([
-3.62741663e+00, -6.30068841e+00, -6.76592886e+00,
-6.97731223e+00, -7.05777777e+00, -6.57305440e+00,
-7.11553046e+00, -7.30569622e+00, -7.45808107e+00,
-7.89984062e+00, -8.11466111e+00, -8.28230086e+00,
-8.79253862e+00, -8.86994311e+00, -9.31299476e+00,
-9.80834199e+00, -9.97333771e+00, -1.08349979e+01,
-1.13199826e+01, -1.22890930e+01, -1.31550471e+01,
-1.42753713e+01, -1.55721098e+01, -1.73811490e+01,
-1.88856303e+01, -2.09570569e+01, -2.30559500e+01,
-2.52127149e+01, -2.76119605e+01, -3.03141128e+01,
-3.30900203e+01, -3.62799730e+01, -3.91169706e+01,
-4.24246421e+01, -4.55740527e+01, -4.92914871e+01,
-5.30682805e+01, -5.70043846e+01, -6.06075991e+01,
-6.45251018e+01, -6.86128365e+01, -7.35896515e+01,
-7.92634608e+01, -8.34733231e+01, -9.29802390e+01,
-1.01179788e+02, -1.10437881e+02, -1.20569527e+02,
-1.31569973e+02, -1.43633399e+02, -1.57534056e+02,
-1.73244610e+02, -1.90066728e+02, -2.07980863e+02,
-2.27589046e+02, -2.50241670e+02, -2.76314755e+02,
-3.04930986e+02, -3.36196973e+02, -3.70907934e+02,
-4.10966644e+02, -4.56684022e+02, -5.06945218e+02,
-5.61838645e+02, -6.22617808e+02, -6.90840503e+02,
-7.67825713e+02, -8.54436805e+02, -9.51106365e+02,
-1.05780305e+03, -1.17435473e+03, -1.30045062e+03,
-1.43571221e+03, -1.57971603e+03, -1.73204264e+03,
-1.88430524e+03, -2.03403679e+03, -2.17861987e+03,
-2.31532884e+03, -8.00654731e+03, -5.21487172e-02,
-2.80234170e-01, 4.93894552e-01, 3.11884938e-01, 6.55799607e-01,
5.62182419e-01, 3.86074983e-01, 3.43741491e-01, 4.22461089e-01,
3.63707951e-01, 4.93150010e-01, 4.72813688e-01, 4.07390308e-01,
4.94974186e-01, 4.69900128e-01, 4.37562389e-01, 5.67370182e-01,
4.88965362e-01, 6.40728461e-01, 6.14619979e-01, 4.97173823e-01,
6.19549666e-01, 6.51193557e-01, 4.48906118e-01, 7.93091492e-01,
6.51249363e-01, 6.56307713e-01, 1.12948552e+00, 9.50018058e-01,
6.79613030e-01, 9.51359123e-01, 6.31059147e-01, 7.97896887e-01,
8.44620817e-01, 7.43683837e-01, 1.56693187e+00, 2.75630011e-01,
5.32956891e-01, 1.57110727e+00, 1.22674610e+00, 4.63932928e-01,
1.47225464e+00, 1.16948107e+00, 1.07965795e+00, -3.20557791e-01,
-1.17064127e+00, -7.84880649e-01, -7.60851182e-01, -1.61415945e+00,
-8.30363975e-01, -1.68459409e+00, -1.49260581e+00, -1.84257084e+00,
-1.72143079e+00, -1.43131579e+00, -1.63719219e+00, -1.43874851e+00,
-1.57207905e+00, -1.72909159e+00, -1.98778122e+00, -1.80843826e+00,
-2.12828312e+00, -2.24768762e+00, -2.36961877e+00, -2.49117258e+00,
-2.59914065e+00, -2.82309085e+00, -2.93613362e+00, -3.34446991e+00,
-3.45445086e+00, -3.74962140e+00, -3.78113417e+00, -4.55643800e+00,
-4.86929016e+00, -5.08657898e+00, -5.22054177e+00, -5.54606515e+00,
-5.78478304e+00, -5.93652041e+00, -6.11519786e+00])
assert(np.allclose(np.array(test_list), np.array(expected_list)))
def test_run_SS(input_path, expected_path):
# Test SS.run_SS function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', input_path))
(income_tax_params, ss_params, iterative_params, chi_params,
small_open_params, baseline, baseline_spending, baseline_dir) =\
input_tuple
p = Specifications()
(p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon,
Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n_ss, tau_payroll,
tau_bq, p.rho, p.omega_SS, p.budget_balance, alpha_T,
p.debt_ratio_ss, tau_b, delta_tau, lambdas, imm_rates, p.e,
retire, p.mean_income_data, h_wealth, p_wealth, m_wealth,
p.b_ellipse, p.upsilon) = ss_params
p.eta = (p.omega_SS.reshape(p.S, 1) *
p.lambdas.reshape(1, p.J)).reshape(1, p.S, p.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.alpha_T = np.ones(p.T + p.S) * alpha_T
p.tau_b = np.ones(p.T + p.S) * tau_b
p.delta_tau = np.ones(p.T + p.S) * delta_tau
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.lambdas = lambdas.reshape(p.J, 1)
p.imm_rates = imm_rates.reshape(1, p.S)
p.tax_func_type = 'DEP'
p.baseline = baseline
p.baseline_spending = baseline_spending
p.baseline_dir = baseline_dir
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.maxiter, p.mindist_SS = iterative_params
p.chi_b, p.chi_n = chi_params
p.small_open, firm_r, hh_r = small_open_params
p.firm_r = np.ones(p.T + p.S) * firm_r
p.hh_r = np.ones(p.T + p.S) * hh_r
p.num_workers = 1
test_dict = SS.run_SS(p, None)
expected_dict = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', expected_path))
# delete values key-value pairs that are not in both dicts
del expected_dict['bssmat'], expected_dict['chi_n'], expected_dict['chi_b']
del expected_dict['Iss_total']
del test_dict['etr_ss'], test_dict['mtrx_ss'], test_dict['mtry_ss']
test_dict['IITpayroll_revenue'] = (test_dict['total_revenue_ss'] -
test_dict['business_revenue'])
del test_dict['T_Pss'], test_dict['T_BQss'], test_dict['T_Wss']
del test_dict['resource_constraint_error'], test_dict['T_Css']
del test_dict['r_gov_ss'], test_dict['r_hh_ss']
del test_dict['K_d_ss'], test_dict['K_f_ss'], test_dict['D_d_ss']
del test_dict['D_f_ss'], test_dict['I_d_ss'], test_dict['Iss_total']
del test_dict['debt_service_f'], test_dict['new_borrowing_f']
test_dict['revenue_ss'] = test_dict.pop('total_revenue_ss')
test_dict['T_Hss'] = test_dict.pop('TR_ss')
for k, v in expected_dict.items():
assert(np.allclose(test_dict[k], v))
'''
Tests of parameter_plots.py module
'''
import pytest
import os
from ogindia import utils, parameter_plots
# Load in test results and parameters
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
base_params = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'model_params_baseline.pkl'))
def test_plot_imm_rates():
fig = parameter_plots.plot_imm_rates(base_params)
assert fig
def test_plot_mort_rates():
fig = parameter_plots.plot_mort_rates(base_params)
assert fig
def test_plot_pop_growth():
fig = parameter_plots.plot_pop_growth(base_params)
assert fig
def test_plot_ability_profiles():
