def test_get_C(c, p, method, expected): """ Test aggregate consumption function. """ C = aggr.get_C(c, p, method) assert np.allclose(C, expected)
def SS_solver(bmat, nmat, r, BQ, TR, factor, Y, p, client, fsolve_flag=False): ''' Solves for the steady state distribution of capital, labor, as well as w, r, TR and the scaling factor, using functional iteration. Args: bmat (Numpy array): initial guess at savings, size = SxJ nmat (Numpy array): initial guess at labor supply, size = SxJ r (scalar): real interest rate BQ (array_like): aggregate bequest amount(s) TR (scalar): lump sum transfer amount factor (scalar): scaling factor converting model units to dollars Y (scalar): real GDP p (OG-Core Specifications object): model parameters client (Dask client object): client Returns: output (dictionary): dictionary with steady state solution results ''' dist = 10 iteration = 0 dist_vec = np.zeros(p.maxiter) maxiter_ss = p.maxiter nu_ss = p.nu if fsolve_flag: # case where already solved via SS_fsolve maxiter_ss = 1 while (dist > p.mindist_SS) and (iteration < maxiter_ss): # Solve for the steady state levels of b and n, given w, r, # Y and factor if p.budget_balance: outer_loop_vars = (bmat, nmat, r, BQ, TR, factor) else: outer_loop_vars = (bmat, nmat, r, BQ, Y, TR, factor) (euler_errors, new_bmat, new_nmat, new_r, new_r_gov, new_r_p, new_w, new_TR, new_Y, new_factor, new_BQ, average_income_model) =\ inner_loop(outer_loop_vars, p, client) r = utils.convex_combo(new_r, r, nu_ss) factor = utils.convex_combo(new_factor, factor, nu_ss) BQ = utils.convex_combo(new_BQ, BQ, nu_ss) if p.baseline_spending: Y = utils.convex_combo(new_Y, Y, nu_ss) if Y != 0: dist = np.array( [utils.pct_diff_func(new_r, r)] + list(utils.pct_diff_func(new_BQ, BQ)) + [utils.pct_diff_func(new_Y, Y)] + [utils.pct_diff_func(new_factor, factor)]).max() else: # If Y is zero (if there is no output), a percent difference # will throw NaN's, so we use an absolute difference dist = np.array( [utils.pct_diff_func(new_r, r)] + list(utils.pct_diff_func(new_BQ, BQ)) + [abs(new_Y - Y)] + [utils.pct_diff_func(new_factor, factor)]).max() else: TR = utils.convex_combo(new_TR, TR, nu_ss) dist = np.array([utils.pct_diff_func(new_r, r)] + list(utils.pct_diff_func(new_BQ, BQ)) + [utils.pct_diff_func(new_TR, TR)] + [utils.pct_diff_func(new_factor, factor)]).max() dist_vec[iteration] = dist # Similar to TPI: if the distance between iterations increases, then # decrease the value of nu to prevent cycling if iteration > 10: if dist_vec[iteration] - dist_vec[iteration - 1] > 0: nu_ss /= 2.0 print('New value of nu:', nu_ss) iteration += 1 if VERBOSE: print('Iteration: %02d' % iteration, ' Distance: ', dist) # Generate the SS values of variables, including euler errors bssmat_s = np.append(np.zeros((1, p.J)), bmat[:-1, :], axis=0) bssmat_splus1 = bmat nssmat = nmat rss = r r_gov_ss = fiscal.get_r_gov(rss, p) TR_ss = TR Lss = aggr.get_L(nssmat, p, 'SS') Bss = aggr.get_B(bssmat_splus1, p, 'SS', False) (Dss, D_d_ss, D_f_ss, new_borrowing, debt_service, new_borrowing_f) = fiscal.get_D_ss(r_gov_ss, Y, p) K_demand_open_ss = firm.get_K(Lss, p.world_int_rate[-1], p, 'SS') Kss, K_d_ss, K_f_ss = aggr.get_K_splits(Bss, K_demand_open_ss, D_d_ss, p.zeta_K[-1]) Yss = firm.get_Y(Kss, Lss, p, 'SS') r_p_ss = aggr.get_r_p(rss, r_gov_ss, Kss, Dss) # Note that implicity in this computation is that immigrants' # wealth is all in the form of private capital I_d_ss = aggr.get_I(bssmat_splus1, K_d_ss, K_d_ss, p, 'SS') Iss = aggr.get_I(bssmat_splus1, Kss, Kss, p, 'SS') wss = new_w BQss = new_BQ factor_ss = factor bqssmat = household.get_bq(BQss, None, p, 'SS') trssmat = household.get_tr(TR_ss, None, p, 'SS') ubissmat = p.ubi_nom_array[-1, :, :] / factor_ss theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, None, p) # Compute effective and marginal tax rates for all agents etr_params_3D = np.tile( np.reshape(p.etr_params[-1, :, :], (p.S, 1, p.etr_params.shape[2])), (1, p.J, 1)) mtrx_params_3D = np.tile( np.reshape(p.mtrx_params[-1, :, :], (p.S, 1, p.mtrx_params.shape[2])), (1, p.J, 1)) mtry_params_3D = np.tile( np.reshape(p.mtry_params[-1, :, :], (p.S, 1, p.mtry_params.shape[2])), (1, p.J, 1)) mtry_ss = tax.MTR_income(r_p_ss, wss, bssmat_s, nssmat, factor, True, p.e, etr_params_3D, mtry_params_3D, p) mtrx_ss = tax.MTR_income(r_p_ss, wss, bssmat_s, nssmat, factor, False, p.e, etr_params_3D, mtrx_params_3D, p) etr_ss = tax.ETR_income(r_p_ss, wss, bssmat_s, nssmat, factor, p.e, etr_params_3D, p) taxss = tax.net_taxes(r_p_ss, wss, bssmat_s, nssmat, bqssmat, factor_ss, trssmat, ubissmat, theta, None, None, False, 'SS', p.e, etr_params_3D, p) cssmat = household.get_cons(r_p_ss, wss, bssmat_s, bssmat_splus1, nssmat, bqssmat, taxss, p.e, p.tau_c[-1, :, :], p) yss_before_tax_mat = household.get_y(r_p_ss, wss, bssmat_s, nssmat, p) Css = aggr.get_C(cssmat, p, 'SS') (total_tax_revenue, iit_payroll_tax_revenue, agg_pension_outlays, UBI_outlays, bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue, business_tax_revenue, payroll_tax_revenue, iit_revenue) = aggr.revenue(r_p_ss, wss, bssmat_s, nssmat, bqssmat, cssmat, Yss, Lss, Kss, factor, ubissmat, theta, etr_params_3D, p, 'SS') Gss = fiscal.get_G_ss(Yss, total_tax_revenue, agg_pension_outlays, TR_ss, UBI_outlays, new_borrowing, debt_service, p) # Compute total investment (not just domestic) Iss_total = aggr.get_I(None, Kss, Kss, p, 'total_ss') # solve resource constraint # net foreign borrowing debt_service_f = fiscal.get_debt_service_f(r_p_ss, D_f_ss) RC = aggr.resource_constraint(Yss, Css, Gss, I_d_ss, K_f_ss, new_borrowing_f, debt_service_f, r_p_ss, p) if VERBOSE: print('Foreign debt holdings = ', D_f_ss) print('Foreign capital holdings = ', K_f_ss) print('resource constraint: ', RC) if Gss < 0: print('Steady state government spending is negative to satisfy' + ' budget') if ENFORCE_SOLUTION_CHECKS and (np.absolute(RC) > p.mindist_SS): print('Resource Constraint Difference:', RC) err = 'Steady state aggregate resource constraint not satisfied' raise RuntimeError(err) # check constraints household.constraint_checker_SS(bssmat_splus1, nssmat, cssmat, p.ltilde) euler_savings = euler_errors[:p.S, :] euler_labor_leisure = euler_errors[p.S:, :] if VERBOSE: print('Maximum error in labor FOC = ', np.absolute(euler_labor_leisure).max()) print('Maximum error in savings FOC = ', np.absolute(euler_savings).max()) # Return dictionary of SS results output = { 'Kss': Kss, 'K_f_ss': K_f_ss, 'K_d_ss': K_d_ss, 'Bss': Bss, 'Lss': Lss, 'Css': Css, 'Iss': Iss, 'Iss_total': Iss_total, 'I_d_ss': I_d_ss, 'nssmat': nssmat, 'Yss': Yss, 'Dss': Dss, 'D_f_ss': D_f_ss, 'D_d_ss': D_d_ss, 'wss': wss, 'rss': rss, 'total_taxes_ss': taxss, 'ubissmat': ubissmat, 'r_gov_ss': r_gov_ss, 'r_p_ss': r_p_ss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss, 'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1, 'yss_before_tax_mat': yss_before_tax_mat, 'bqssmat': bqssmat, 'TR_ss': TR_ss, 'trssmat': trssmat, 'Gss': Gss, 'total_tax_revenue': total_tax_revenue, 'business_tax_revenue': business_tax_revenue, 'iit_payroll_tax_revenue': iit_payroll_tax_revenue, 'iit_revenue': iit_revenue, 'payroll_tax_revenue': payroll_tax_revenue, 'agg_pension_outlays': agg_pension_outlays, 'UBI_outlays_SS': UBI_outlays, 'bequest_tax_revenue': bequest_tax_revenue, 'wealth_tax_revenue': wealth_tax_revenue, 'cons_tax_revenue': cons_tax_revenue, 'euler_savings': euler_savings, 'debt_service_f': debt_service_f, 'new_borrowing_f': new_borrowing_f, 'debt_service': debt_service, 'new_borrowing': new_borrowing, 'euler_labor_leisure': euler_labor_leisure, 'resource_constraint_error': RC, 'etr_ss': etr_ss, 'mtrx_ss': mtrx_ss, 'mtry_ss': mtry_ss } return output
def run_TPI(p, client=None): ''' Solve for transition path equilibrium of OG-Core. Args: p (OG-Core Specifications object): model parameters client (Dask client object): client Returns: output (dictionary): dictionary with transition path solution results ''' # unpack tuples of parameters initial_values, ss_vars, theta, baseline_values = get_initial_SS_values(p) (B0, b_sinit, b_splus1init, factor, initial_b, initial_n) =\ initial_values (TRbaseline, Gbaseline, D0_baseline) = baseline_values # Create time path of UBI household benefits and aggregate UBI outlays ubi = p.ubi_nom_array / factor UBI = aggr.get_L(ubi[:p.T], p, 'TPI') print('Government spending breakpoints are tG1: ', p.tG1, '; and tG2:', p.tG2) # Initialize guesses at time paths # Make array of initial guesses for labor supply and savings guesses_b = utils.get_initial_path(initial_b, ss_vars['bssmat_splus1'], p, 'ratio') guesses_n = utils.get_initial_path(initial_n, ss_vars['nssmat'], p, 'ratio') b_mat = guesses_b n_mat = guesses_n ind = np.arange(p.S) # Get path for aggregate savings and labor supply L_init = np.ones((p.T + p.S, )) * ss_vars['Lss'] B_init = np.ones((p.T + p.S, )) * ss_vars['Bss'] L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI') B_init[1:p.T] = aggr.get_B(b_mat[:p.T], p, 'TPI', False)[:p.T - 1] B_init[0] = B0 K_init = B_init * ss_vars['Kss'] / ss_vars['Bss'] K = K_init K_d = K_init * ss_vars['K_d_ss'] / ss_vars['Kss'] K_f = K_init * ss_vars['K_f_ss'] / ss_vars['Kss'] L = L_init B = B_init Y = np.zeros_like(K) Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI') Y[p.T:] = ss_vars['Yss'] r = np.zeros_like(Y) r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI') r[p.T:] = ss_vars['rss'] # For case where economy is small open econ r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1] # Compute other interest rates r_gov = fiscal.get_r_gov(r, p) r_p = aggr.get_r_p(r, r_gov, K, ss_vars['Dss']) # compute w w = np.zeros_like(r) w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI') w[p.T:] = ss_vars['wss'] # initial guesses at fiscal vars if p.budget_balance: if np.abs(ss_vars['TR_ss']) < 1e-13: TR_ss2 = 0.0 # sometimes SS is very small but not zero, # even if taxes are zero, this get's rid of the # approximation error, which affects the pct changes below else: TR_ss2 = ss_vars['TR_ss'] TR = np.ones(p.T + p.S) * TR_ss2 total_tax_revenue = TR - ss_vars['agg_pension_outlays'] G = np.zeros(p.T + p.S) D = np.zeros(p.T + p.S) D_d = np.zeros(p.T + p.S) D_f = np.zeros(p.T + p.S) else: if p.baseline_spending: TR = TRbaseline G = Gbaseline G[p.T:] = ss_vars['Gss'] else: TR = p.alpha_T * Y G = np.ones(p.T + p.S) * ss_vars['Gss'] D = np.ones(p.T + p.S) * ss_vars['Dss'] D_d = D * ss_vars['D_d_ss'] / ss_vars['Dss'] D_f = D * ss_vars['D_f_ss'] / ss_vars['Dss'] total_tax_revenue = np.ones(p.T + p.S) * ss_vars['total_tax_revenue'] # Initialize bequests BQ0 = aggr.get_BQ(r_p[0], initial_b, None, p, 'SS', True) if not p.use_zeta: BQ = np.zeros((p.T + p.S, p.J)) for j in range(p.J): BQ[:, j] = (list(np.linspace(BQ0[j], ss_vars['BQss'][j], p.T)) + [ss_vars['BQss'][j]] * p.S) BQ = np.array(BQ) else: BQ = (list(np.linspace(BQ0, ss_vars['BQss'], p.T)) + [ss_vars['BQss']] * p.S) BQ = np.array(BQ) TPIiter = 0 TPIdist = 10 euler_errors = np.zeros((p.T, 2 * p.S, p.J)) TPIdist_vec = np.zeros(p.maxiter) # TPI loop while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI): r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p) if not p.budget_balance: K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI') r_p[:p.T] = aggr.get_r_p(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T]) outer_loop_vars = (r, w, r_p, BQ, TR, theta) euler_errors = np.zeros((p.T, 2 * p.S, p.J)) lazy_values = [] for j in range(p.J): guesses = (guesses_b[:, :, j], guesses_n[:, :, j]) lazy_values.append( delayed(inner_loop)(guesses, outer_loop_vars, initial_values, ubi, j, ind, p)) if client: futures = client.compute(lazy_values, num_workers=p.num_workers) results = client.gather(futures) else: results = results = compute(*lazy_values, scheduler=dask.multiprocessing.get, num_workers=p.num_workers) for j, result in enumerate(results): euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result bmat_s = np.zeros((p.T, p.S, p.J)) bmat_s[0, 1:, :] = initial_b[:-1, :] bmat_s[1:, 1:, :] = b_mat[:p.T - 1, :-1, :] bmat_splus1 = np.zeros((p.T, p.S, p.J)) bmat_splus1[:, :, :] = b_mat[:p.T, :, :] etr_params_4D = np.tile( p.etr_params[:p.T, :, :].reshape(p.T, p.S, 1, p.etr_params.shape[2]), (1, 1, p.J, 1)) bqmat = household.get_bq(BQ, None, p, 'TPI') trmat = household.get_tr(TR, None, p, 'TPI') tax_mat = tax.net_taxes(r_p[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor, trmat[:p.T, :, :], ubi[:p.T, :, :], theta, 0, None, False, 'TPI', p.e, etr_params_4D, p) r_p_path = utils.to_timepath_shape(r_p) wpath = utils.to_timepath_shape(w) c_mat = household.get_cons(r_p_path[:p.T, :, :], wpath[:p.T, :, :], bmat_s, bmat_splus1, n_mat[:p.T, :, :], bqmat[:p.T, :, :], tax_mat, p.e, p.tau_c[:p.T, :, :], p) y_before_tax_mat = household.get_y(r_p_path[:p.T, :, :], wpath[:p.T, :, :], bmat_s[:p.T, :, :], n_mat[:p.T, :, :], p) (total_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays, UBI_outlays, bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue, business_tax_revenue, payroll_tax_revenue, iit_revenue) = aggr.revenue(r_p[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :], bqmat[:p.T, :, :], c_mat[:p.T, :, :], Y[:p.T], L[:p.T], K[:p.T], factor, ubi[:p.T, :, :], theta, etr_params_4D, p, 'TPI') total_tax_revenue[:p.T] = total_tax_rev dg_fixed_values = (Y, total_tax_revenue, agg_pension_outlays, UBI_outlays, TR, Gbaseline, D0_baseline) (Dnew, G[:p.T], D_d[:p.T], D_f[:p.T], new_borrowing, debt_service, new_borrowing_f) =\ fiscal.D_G_path(r_gov, dg_fixed_values, p) L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI') B[1:p.T] = aggr.get_B(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1] K_demand_open = firm.get_K(L[:p.T], p.world_int_rate[:p.T], p, 'TPI') K[:p.T], K_d[:p.T], K_f[:p.T] = aggr.get_K_splits( B[:p.T], K_demand_open, D_d[:p.T], p.zeta_K[:p.T]) Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI') rnew = r.copy() rnew[:p.T] = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI') # For case where economy is small open econ r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1] r_gov_new = fiscal.get_r_gov(rnew, p) r_p_new = aggr.get_r_p(rnew[:p.T], r_gov_new[:p.T], K[:p.T], Dnew[:p.T]) # compute w wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI') b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)), b_mat[:p.T - 1, :, :], axis=0) BQnew = aggr.get_BQ(r_p_new[:p.T], b_mat_shift, None, p, 'TPI', False) bqmat_new = household.get_bq(BQnew, None, p, 'TPI') (total_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays, UBI_outlays, bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue, business_tax_revenue, payroll_tax_revenue, iit_revenue) = aggr.revenue(r_p_new[:p.T], wnew[:p.T], bmat_s, n_mat[:p.T, :, :], bqmat_new[:p.T, :, :], c_mat[:p.T, :, :], Ynew[:p.T], L[:p.T], K[:p.T], factor, ubi[:p.T, :, :], theta, etr_params_4D, p, 'TPI') total_tax_revenue[:p.T] = total_tax_rev TR_new = fiscal.get_TR(Ynew[:p.T], TR[:p.T], G[:p.T], total_tax_revenue[:p.T], agg_pension_outlays[:p.T], UBI_outlays[:p.T], p, 'TPI') # update vars for next iteration w[:p.T] = wnew[:p.T] r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu) BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu) D[:p.T] = Dnew[:p.T] Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu) if not p.baseline_spending: TR[:p.T] = utils.convex_combo(TR_new[:p.T], TR[:p.T], p.nu) guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu) guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu) print('r diff: ', (rnew[:p.T] - r[:p.T]).max(), (rnew[:p.T] - r[:p.T]).min()) print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(), (BQnew[:p.T] - BQ[:p.T]).min()) print('TR diff: ', (TR_new[:p.T] - TR[:p.T]).max(), (TR_new[:p.T] - TR[:p.T]).min()) print('Y diff: ', (Ynew[:p.T] - Y[:p.T]).max(), (Ynew[:p.T] - Y[:p.T]).min()) if not p.baseline_spending: if TR.all() != 0: TPIdist = np.array( list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list( utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) + list(utils.pct_diff_func(TR_new[:p.T], TR[:p.T]))).max() else: TPIdist = np.array( list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list( utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) + list(np.abs(TR[:p.T]))).max() else: TPIdist = np.array( list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) + list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max() TPIdist_vec[TPIiter] = TPIdist # After T=10, if cycling occurs, drop the value of nu # wait til after T=10 or so, because sometimes there is a jump up # in the first couple iterations # if TPIiter > 10: # if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0: # nu /= 2 # print 'New Value of nu:', nu TPIiter += 1 print('Iteration:', TPIiter) print('\tDistance:', TPIdist) # Compute effective and marginal tax rates for all agents mtrx_params_4D = np.tile( p.mtrx_params[:p.T, :, :].reshape(p.T, p.S, 1, p.mtrx_params.shape[2]), (1, 1, p.J, 1)) mtry_params_4D = np.tile( p.mtry_params[:p.T, :, :].reshape(p.T, p.S, 1, p.mtry_params.shape[2]), (1, 1, p.J, 1)) e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1)) mtry_path = tax.MTR_income(r_p_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor, True, e_3D, etr_params_4D, mtry_params_4D, p) mtrx_path = tax.MTR_income(r_p_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor, False, e_3D, etr_params_4D, mtrx_params_4D, p) etr_path = tax.ETR_income(r_p_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor, e_3D, etr_params_4D, p) C = aggr.get_C(c_mat, p, 'TPI') # Note that implicity in this computation is that immigrants' # wealth is all in the form of private capital I_d = aggr.get_I(bmat_splus1[:p.T], K_d[1:p.T + 1], K_d[:p.T], p, 'TPI') I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI') # solve resource constraint # foreign debt service costs debt_service_f = fiscal.get_debt_service_f(r_p, D_f) RC_error = aggr.resource_constraint(Y[:p.T - 1], C[:p.T - 1], G[:p.T - 1], I_d[:p.T - 1], K_f[:p.T - 1], new_borrowing_f[:p.T - 1], debt_service_f[:p.T - 1], r_p[:p.T - 1], p) # Compute total investment (not just domestic) I_total = aggr.get_I(None, K[1:p.T + 1], K[:p.T], p, 'total_tpi') # Compute resource constraint error rce_max = np.amax(np.abs(RC_error)) print('Max absolute value resource constraint error:', rce_max) print('Checking time path for violations of constraints.') for t in range(p.T): household.constraint_checker_TPI(b_mat[t], n_mat[t], c_mat[t], t, p.ltilde) eul_savings = euler_errors[:, :p.S, :].max(1).max(1) eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1) print('Max Euler error, savings: ', eul_savings) print('Max Euler error labor supply: ', eul_laborleisure) ''' ------------------------------------------------------------------------ Save variables/values so they can be used in other modules ------------------------------------------------------------------------ ''' output = { 'Y': Y[:p.T], 'B': B, 'K': K, 'K_f': K_f, 'K_d': K_d, 'L': L, 'C': C, 'I': I, 'I_total': I_total, 'I_d': I_d, 'BQ': BQ, 'total_tax_revenue': total_tax_revenue, 'business_tax_revenue': business_tax_revenue, 'iit_payroll_tax_revenue': iit_payroll_tax_revenue, 'iit_revenue': iit_revenue, 'payroll_tax_revenue': payroll_tax_revenue, 'TR': TR, 'agg_pension_outlays': agg_pension_outlays, 'bequest_tax_revenue': bequest_tax_revenue, 'wealth_tax_revenue': wealth_tax_revenue, 'cons_tax_revenue': cons_tax_revenue, 'G': G, 'D': D, 'D_f': D_f, 'D_d': D_d, 'r': r, 'r_gov': r_gov, 'r_p': r_p, 'w': w, 'bmat_splus1': bmat_splus1, 'bmat_s': bmat_s[:p.T, :, :], 'n_mat': n_mat[:p.T, :, :], 'c_path': c_mat, 'bq_path': bqmat, 'tr_path': trmat, 'y_before_tax_mat': y_before_tax_mat, 'tax_path': tax_mat, 'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure, 'resource_constraint_error': RC_error, 'new_borrowing_f': new_borrowing_f, 'debt_service_f': debt_service_f, 'etr_path': etr_path, 'mtrx_path': mtrx_path, 'mtry_path': mtry_path, 'ubi_path': ubi, 'UBI_path': UBI } tpi_dir = os.path.join(p.output_base, "TPI") utils.mkdirs(tpi_dir) tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl") with open(tpi_vars, "wb") as f: pickle.dump(output, f) if np.any(G) < 0: print('Government spending is negative along transition path' + ' to satisfy budget') if (((TPIiter >= p.maxiter) or (np.absolute(TPIdist) > p.mindist_TPI)) and ENFORCE_SOLUTION_CHECKS): raise RuntimeError('Transition path equlibrium not found' + ' (TPIdist)') if ((np.any(np.absolute(RC_error) >= p.mindist_TPI * 10)) and ENFORCE_SOLUTION_CHECKS): raise RuntimeError('Transition path equlibrium not found ' + '(RC_error)') if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or (np.any(np.absolute(eul_laborleisure) > p.mindist_TPI))) and ENFORCE_SOLUTION_CHECKS): raise RuntimeError('Transition path equlibrium not found ' + '(eulers)') return output