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['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 w_path, r_path, T_H_path, BQ_path, Y_path = TPI.run_time_path_iteration( **ss_outputs) TPI.TP_solutions(w_path, r_path, T_H_path, BQ_path, **ss_outputs) quit() # # Use fsolve to find time path # 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 = parameters # Kss = ss_outputs['Kss'] # Lss = ss_outputs['Lss'] # T_Hss = ss_outputs['T_Hss'] # BQss = ss_outputs['BQss'] # # Initialize Time paths # domain = np.linspace(0, T, T) # Kinit = (-1 / (domain + 1)) * (Kss - K0) + Kss
def runner(output_base, input_dir, baseline=False, analytical_mtrs=True, reform={}, user_params={}, guid='', run_micro=True): from ogusa import parameters, wealth, labor, demographics, income 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, 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) globals().update(run_params) from ogusa import SS, TPI # Generate Wealth data moments wealth.get_wealth_data(lambdas, J, flag_graphs, output_dir=input_dir) # Generate labor data moments labor.labor_data_moments(flag_graphs, output_dir=input_dir) get_baseline = True 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', 'calibrate_model', 'retire', 'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth', 'm_wealth', 'get_baseline', 'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho' ] ''' ------------------------------------------------------------------------ Run SS with minimization to fit chi_b and chi_n ------------------------------------------------------------------------ ''' # This is the simulation before getting the replacement rate values sim_params = {} glbs = globals() lcls = locals() for key in param_names: if key in glbs: sim_params[key] = glbs[key] else: sim_params[key] = lcls[key] sim_params['output_dir'] = input_dir sim_params['run_params'] = run_params income_tax_params, wealth_tax_params, ellipse_params, ss_parameters, iterative_params = SS.create_steady_state_parameters( **sim_params) ss_outputs = SS.run_steady_state(income_tax_params, ss_parameters, iterative_params, get_baseline, calibrate_model, output_dir=input_dir) ''' ------------------------------------------------------------------------ Run the baseline TPI simulation ------------------------------------------------------------------------ ''' ss_outputs['get_baseline'] = get_baseline sim_params['input_dir'] = input_dir income_tax_params, wealth_tax_params, ellipse_params, parameters, N_tilde, omega_stationary, K0, b_sinit, \ b_splus1init, L0, Y0, w0, r0, BQ0, T_H_0, tax0, c0, initial_b, initial_n = 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['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'] = input_dir 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_time_path_iteration( **ss_outputs) 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, 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)