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 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)))
r = 0.04
w = 1.2
factor = 100000
# test_ETR_income = tax.ETR_income(r, w, b, n, factor,
# (e, etr_params, tax_func_type))
test_ETR_income = tax.ETR_income(r, w, b, n, factor, params.e,
etr_params, params)
assert np.allclose(test_ETR_income, expected)
p1 = Specifications()
p1.e = np.array([0.5, 0.45, 0.3])
p1.S = 3
p1.J = 1
p1.tax_func_type = 'DEP'
p1.analytical_mtrs = True
etr_params1 = np.reshape(np.array([
[0.001, 0.002, 0.003, 0.0015, 0.8,
-0.14, 0.8, -0.15, 0.15, 0.16, -0.15,
0.83],
[0.002, 0.001, 0.002, 0.04, 0.8, -0.14,
0.8, -0.15, 0.15, 0.16, -0.15, 0.83],
[0.011, 0.001, 0.003, 0.06, 0.8, -0.14,
0.8, -0.15, 0.15, 0.16, -0.15, 0.83]]), (1, p1.S, 12))
mtrx_params1 = np.reshape(np.array([
[0.001, 0.002, 0.003, 0.0015, 0.68,
-0.17, 0.8, -0.42, 0.18, 0.43, -0.42,
0.96],
[0.001, 0.002, 0.003, 0.0015, 0.65,
-0.17, 0.8, -0.42, 0.18, 0.33, -0.12,
0.90],
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))
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
p1.m_wealth = np.ones(p1.T + p1.S) * m_wealth
p1.retire = (np.ones(p1.T + p1.S) * retire).astype(int)
p1.lambdas = lambdas.reshape(p1.J, 1)
p1.imm_rates = imm_rates.reshape(1, p1.S)
p1.tax_func_type = 'DEP'
p1.baseline = True
p1.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p1.etr_params = np.transpose(etr_params.reshape(
p1.S, 1, etr_params.shape[-1]), (1, 0, 2))
p1.mtrx_params = np.transpose(mtrx_params.reshape(
p1.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p1.mtry_params = np.transpose(mtry_params.reshape(
p1.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p1.maxiter, p1.mindist_SS = iterative_params
p1.chi_b, p1.chi_n = chi_params
p1.small_open, firm_r, hh_r = small_open_params
p1.firm_r = np.ones(p1.T + p1.S) * firm_r
p1.hh_r = np.ones(p1.T + p1.S) * hh_r
p1.num_workers = 1
BQ1 = np.ones((p1.J)) * 0.00019646295986015257
guesses1 = [guesses_in[0]] + list(BQ1) + [guesses_in[1]] + [guesses_in[2]]
args1 = (bssmat, nssmat, None, None, p1, client)
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))
# Define variables for test of SS version
p1 = Specifications()
p1.e = np.array([1.0, 0.9, 1.4]).reshape(3, 1)
p1.sigma = 2.0
p1.beta = 0.96
p1.g_y = 0.03
p1.chi_b = np.array([1.5])
p1.tau_bq = np.array([0.0])
p1.rho = np.array([0.1, 0.2, 1.0])
p1.lambdas = np.array([1.0])
p1.J = 1
p1.S = 3
p1.T = 3
p1.analytical_mtrs = False
etr_params = np.array([
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.33, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.20, 0]]),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0]]),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0]])
])
mtry_params = np.array([
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.28, 0]]),