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))
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)
expected1 = np.array([0.28753454, 0.01889046, 0.02472582, 0.02669199,
0.01631467, 0.01925092, 0.02206471, 0.00407802,
-0.07014671494961716, 0.00626609])
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'SS_fsolve_reform_inputs.pkl'))
guesses_in2, params = input_tuple
params = params + (None, 1)
def test_get_cons(model_args, expected):
# Test consumption calculation
r, w, b, b_splus1, n, bq, net_tax, tau_c, p = model_args
test_value = household.get_cons(r, w, b, b_splus1, n, bq, net_tax, p.e,
tau_c, p)
assert np.allclose(test_value, expected)
# 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],