def function_to_minimize(chi_params_scalars, chi_params_init, params, iterative_params, weights_SS, rho_vec, lambdas, tau_bq, e): ''' Inputs: chi_params_scalars = guesses for multipliers for chi parameters ((S+J)x1 array) chi_params_init = chi parameters that will be multiplied ((S+J)x1 array) params = list of parameters (list) weights_SS = steady state population weights (Sx1 array) rho_vec = mortality rates (Sx1 array) lambdas = ability weights (Jx1 array) tau_bq = bequest tax rates (Jx1 array) e = ability levels (Jx1 array) Output: The sum of absolute percent deviations between the actual and simulated wealth moments ''' J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \ a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params chi_params_init *= chi_params_scalars # print 'Print Chi_b: ', chi_params_init[:J] # print 'Scaling vals:', chi_params_scalars[:J] solutions_dict = pickle.load(open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "rb")) solutions = solutions_dict['solutions'] b_guess = solutions[:S*J] n_guess = solutions[S*J:2*S*J] wguess, rguess, factorguess, T_Hguess = solutions[2*S*J:] solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params_init[J:], chi_params_init[:J], params, iterative_params, tau_bq, rho, lambdas, weights_SS, e) b_new = solutions[:S*J] n_new = solutions[S*J:2*S*J] w_new, r_new, factor_new, T_H_new = solutions[2*S*J:] # Wealth Calibration Euler error5 = list(utils.check_wealth_calibration(b_new.reshape(S, J)[:-1, :], factor_new, params)) # labor calibration euler lab_data_dict = pickle.load(open("OUTPUT/Saved_moments/labor_data_moments.pkl", "rb")) labor_sim = (n_new.reshape(S, J)*lambdas.reshape(1, J)).sum(axis=1) if DATASET == 'SMALL': lab_dist_data = lab_data_dict['labor_dist_data'][:S] else: lab_dist_data = lab_data_dict['labor_dist_data'] error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data)) # combine eulers output = np.array(error5 + error6) # Constraints eul_error = np.ones(J) for j in xrange(J): eul_error[j] = np.abs(Euler_equation_solver(np.append(b_new.reshape(S, J)[:, j], n_new.reshape(S, J)[:, j]), r_new, w_new, T_H_new, factor_new, j, params, chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas, weights_SS, e)).max() fsolve_no_converg = eul_error.max() if np.isnan(fsolve_no_converg): fsolve_no_converg = 1e6 if fsolve_no_converg > 1e-4: # If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be # and don't save the solutions as initial guesses (since they might be gibberish) output += 1e14 else: var_names = ['solutions'] dictionary = {} for key in var_names: dictionary[key] = locals()[key] pickle.dump(dictionary, open("OUTPUT/Saved_moments/SS_init_solutions.pkl", "wb")) if (chi_params_init <= 0.0).any(): # In case the minimizer doesn't respect the bounds given output += 1e14 # Use generalized method of moments to fit the chi's weighting_mat = np.eye(2*J + S) scaling_val = 100.0 value = np.dot(scaling_val * np.dot(output.reshape(1, 2*J+S), weighting_mat), scaling_val * output.reshape(2*J+S, 1)) print 'Value of criterion function: ', value.sum() return value.sum()
def function_to_minimize(chi_params_scalars, chi_params_init, income_tax_parameters, ss_parameters, iterative_params, weights_SS, rho_vec, lambdas, tau_bq, e, output_dir): ''' Inputs: chi_params_scalars = guesses for multipliers for chi parameters ((S+J)x1 array) chi_params_init = chi parameters that will be multiplied ((S+J)x1 array) params = list of parameters (list) weights_SS = steady state population weights (Sx1 array) rho_vec = mortality rates (Sx1 array) lambdas = ability weights (Jx1 array) tau_bq = bequest tax rates (Jx1 array) e = ability levels (Jx1 array) Output: The sum of absolute percent deviations between the actual and simulated wealth moments ''' J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\ g_n_ss, tau_payroll, retire, mean_income_data,\ h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters chi_params_init *= chi_params_scalars # print 'Print Chi_b: ', chi_params_init[:J] # print 'Scaling vals:', chi_params_scalars[:J] ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_solutions.pkl") solutions_dict = pickle.load(open(ss_init_path, "rb")) solutions = solutions_dict['solutions'] b_guess = solutions[:(S * J)] n_guess = solutions[S * J:2 * S * J] wguess, rguess, factorguess, T_Hguess = solutions[(2 * S * J):] guesses = [wguess, rguess, T_Hguess, factorguess] args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params_init[J:], chi_params_init[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e) [solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True) [wguess, rguess, T_Hguess, factorguess] = solutions fsolve_flag = True solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params_init[ J:], chi_params_init[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag) b_new = solutions[:(S * J)] n_new = solutions[(S * J):(2 * S * J)] w_new, r_new, factor_new, T_H_new = solutions[(2 * S * J):] # Wealth Calibration Euler error5 = list(utils.check_wealth_calibration(b_new.reshape(S, J)[:-1, :], factor_new, ss_parameters, output_dir)) # labor calibration euler labor_path = os.path.join( output_dir, "Saved_moments/labor_data_moments.pkl") lab_data_dict = pickle.load(open(labor_path, "rb")) labor_sim = (n_new.reshape(S, J) * lambdas.reshape(1, J)).sum(axis=1) if DATASET == 'SMALL': lab_dist_data = lab_data_dict['labor_dist_data'][:S] else: lab_dist_data = lab_data_dict['labor_dist_data'] error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data)) # combine eulers output = np.array(error5 + error6) # Constraints eul_error = np.ones(J) for j in xrange(J): eul_error[j] = np.abs(Euler_equation_solver(np.append(b_new.reshape(S, J)[:, j], n_new.reshape(S, J)[:, j]), r_new, w_new, T_H_new, factor_new, j, income_tax_parameters, ss_parameters, chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas, weights_SS, e)).max() fsolve_no_converg = eul_error.max() if np.isnan(fsolve_no_converg): fsolve_no_converg = 1e6 if fsolve_no_converg > 1e-4: # If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be # and don't save the solutions as initial guesses (since they might be # gibberish) output += 1e14 else: var_names = ['solutions'] dictionary = {} for key in var_names: dictionary[key] = locals()[key] ss_init_path = os.path.join( output_dir, "Saved_moments/SS_init_solutions.pkl") pickle.dump(dictionary, open(ss_init_path, "wb")) if (chi_params_init <= 0.0).any(): # In case the minimizer doesn't respect the bounds given output += 1e14 # Use generalized method of moments to fit the chi's weighting_mat = np.eye(2 * J + S) scaling_val = 100.0 value = np.dot(scaling_val * np.dot(output.reshape(1, 2 * J + S), weighting_mat), scaling_val * output.reshape(2 * J + S, 1)) print 'Value of criterion function: ', value.sum() # pickle output in case not converge global Nfeval, value_all, chi_params_all value_all[Nfeval] = value.sum() chi_params_all[:,Nfeval] = chi_params_init dict_GMM = dict([('values', value_all), ('chi_params', chi_params_all)]) ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_all.pkl") pickle.dump(dict_GMM, open(ss_init_path, "wb")) Nfeval += 1 return value.sum()
def function_to_minimize(chi_params_scalars, chi_params_init, income_tax_parameters, ss_parameters, iterative_params, weights_SS, rho_vec, lambdas, tau_bq, e, output_dir): ''' Inputs: chi_params_scalars = guesses for multipliers for chi parameters ((S+J)x1 array) chi_params_init = chi parameters that will be multiplied ((S+J)x1 array) params = list of parameters (list) weights_SS = steady state population weights (Sx1 array) rho_vec = mortality rates (Sx1 array) lambdas = ability weights (Jx1 array) tau_bq = bequest tax rates (Jx1 array) e = ability levels (Jx1 array) Output: The sum of absolute percent deviations between the actual and simulated wealth moments ''' J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\ g_n_ss, tau_payroll, retire, mean_income_data,\ h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters chi_params_init *= chi_params_scalars # print 'Print Chi_b: ', chi_params_init[:J] # print 'Scaling vals:', chi_params_scalars[:J] ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_solutions.pkl") solutions_dict = pickle.load(open(ss_init_path, "rb")) solutions = solutions_dict['solutions'] b_guess = solutions[:(S * J)] n_guess = solutions[S * J:2 * S * J] wguess, rguess, factorguess, T_Hguess = solutions[(2 * S * J):] guesses = [wguess, rguess, T_Hguess, factorguess] args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params_init[J:], chi_params_init[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e) [solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True) [wguess, rguess, T_Hguess, factorguess] = solutions fsolve_flag = True solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params_init[J:], chi_params_init[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag) b_new = solutions[:(S * J)] n_new = solutions[(S * J):(2 * S * J)] w_new, r_new, factor_new, T_H_new = solutions[(2 * S * J):] # Wealth Calibration Euler error5 = list( utils.check_wealth_calibration( b_new.reshape(S, J)[:-1, :], factor_new, ss_parameters, output_dir)) # labor calibration euler labor_path = os.path.join(output_dir, "Saved_moments/labor_data_moments.pkl") lab_data_dict = pickle.load(open(labor_path, "rb")) labor_sim = (n_new.reshape(S, J) * lambdas.reshape(1, J)).sum(axis=1) if DATASET == 'SMALL': lab_dist_data = lab_data_dict['labor_dist_data'][:S] else: lab_dist_data = lab_data_dict['labor_dist_data'] error6 = list(utils.perc_dif_func(labor_sim, lab_dist_data)) # combine eulers output = np.array(error5 + error6) # Constraints eul_error = np.ones(J) for j in xrange(J): eul_error[j] = np.abs( Euler_equation_solver( np.append( b_new.reshape(S, J)[:, j], n_new.reshape(S, J)[:, j]), r_new, w_new, T_H_new, factor_new, j, income_tax_parameters, ss_parameters, chi_params_init[:J], chi_params_init[J:], tau_bq, rho, lambdas, weights_SS, e)).max() fsolve_no_converg = eul_error.max() if np.isnan(fsolve_no_converg): fsolve_no_converg = 1e6 if fsolve_no_converg > 1e-4: # If the fsovle didn't converge (was NaN or above the tolerance), then tell the minimizer that this is a bad place to be # and don't save the solutions as initial guesses (since they might be # gibberish) output += 1e14 else: var_names = ['solutions'] dictionary = {} for key in var_names: dictionary[key] = locals()[key] ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_solutions.pkl") pickle.dump(dictionary, open(ss_init_path, "wb")) if (chi_params_init <= 0.0).any(): # In case the minimizer doesn't respect the bounds given output += 1e14 # Use generalized method of moments to fit the chi's weighting_mat = np.eye(2 * J + S) scaling_val = 100.0 value = np.dot( scaling_val * np.dot(output.reshape(1, 2 * J + S), weighting_mat), scaling_val * output.reshape(2 * J + S, 1)) print 'Value of criterion function: ', value.sum() # pickle output in case not converge global Nfeval, value_all, chi_params_all value_all[Nfeval] = value.sum() chi_params_all[:, Nfeval] = chi_params_init dict_GMM = dict([('values', value_all), ('chi_params', chi_params_all)]) ss_init_path = os.path.join(output_dir, "Saved_moments/SS_init_all.pkl") pickle.dump(dict_GMM, open(ss_init_path, "wb")) Nfeval += 1 return value.sum()