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_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))
p3.lambdas.reshape(1, p3.J)).reshape(1, p3.S, p3.J) p3.Z = np.ones(p3.T + p3.S) * Z p3.tau_bq = np.ones(p3.T + p3.S) * 0.0 p3.tau_payroll = np.ones(p3.T + p3.S) * tau_payroll p3.alpha_T = np.ones(p3.T + p3.S) * alpha_T p3.tau_b = np.ones(p3.T + p3.S) * tau_b p3.delta_tau = np.ones(p3.T + p3.S) * delta_tau p3.h_wealth = np.ones(p3.T + p3.S) * h_wealth p3.p_wealth = np.ones(p3.T + p3.S) * p_wealth p3.m_wealth = np.ones(p3.T + p3.S) * m_wealth p3.retire = (np.ones(p3.T + p3.S) * retire).astype(int) p3.lambdas = lambdas.reshape(p3.J, 1) p3.imm_rates = imm_rates.reshape(1, p3.S) p3.tax_func_type = 'DEP' p3.baseline = False p3.baseline_spending = True p3.analytical_mtrs, etr_params, mtrx_params, mtry_params =\ income_tax_params p3.etr_params = np.transpose(etr_params.reshape( p3.S, 1, etr_params.shape[-1]), (1, 0, 2)) p3.mtrx_params = np.transpose(mtrx_params.reshape( p3.S, 1, mtrx_params.shape[-1]), (1, 0, 2)) p3.mtry_params = np.transpose(mtry_params.reshape( p3.S, 1, mtry_params.shape[-1]), (1, 0, 2)) p3.maxiter, p3.mindist_SS = iterative_params p3.chi_b, p3.chi_n = chi_params p3.small_open, firm_r, hh_r = small_open_params p3.firm_r = np.ones(p3.T + p3.S) * firm_r p3.hh_r = np.ones(p3.T + p3.S) * hh_r p3.num_workers = 1 BQ3 = np.ones((p3.J)) * 0.00019646295986015257
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))