test_data,
ids=['scalar', 'vector', 'matrix', '3D array'])
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],
def test_firstdoughnutring(dask_client):
# Test TPI.firstdoughnutring 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', 'firstdoughnutring_inputs.pkl'))
guesses, r, w, b, BQ, TR, j, params = input_tuple
income_tax_params, tpi_params, initial_b = params
tpi_params = tpi_params + [True]
p = Specifications(client=dask_client, num_workers=NUM_WORKERS)
(p.J, p.S, p.T, p.BW, 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.beta = np.ones(p.J) * beta
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.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.tax_func_type = 'DEP'
p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\
income_tax_params
p.etr_params = np.transpose(etr_params, (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params, (1, 0, 2))
p.mtry_params = np.transpose(mtry_params, (1, 0, 2))
etr_params_old = p.etr_params
mtrx_params_old = p.mtrx_params
mtry_params_old = p.mtry_params
p.etr_params = etr_params_old.copy()
p.etr_params[:, :, 5] = etr_params_old[:, :, 6]
p.etr_params[:, :, 6] = etr_params_old[:, :, 11]
p.etr_params[:, :, 7] = etr_params_old[:, :, 5]
p.etr_params[:, :, 8] = etr_params_old[:, :, 7]
p.etr_params[:, :, 9] = etr_params_old[:, :, 8]
p.etr_params[:, :, 10] = etr_params_old[:, :, 9]
p.etr_params[:, :, 11] = etr_params_old[:, :, 10]
p.mtrx_params = mtrx_params_old.copy()
p.mtrx_params[:, :, 5] = mtrx_params_old[:, :, 6]
p.mtrx_params[:, :, 6] = mtrx_params_old[:, :, 11]
p.mtrx_params[:, :, 7] = mtrx_params_old[:, :, 5]
p.mtrx_params[:, :, 8] = mtrx_params_old[:, :, 7]
p.mtrx_params[:, :, 9] = mtrx_params_old[:, :, 8]
p.mtrx_params[:, :, 10] = mtrx_params_old[:, :, 9]
p.mtrx_params[:, :, 11] = mtrx_params_old[:, :, 10]
p.mtry_params = mtry_params_old.copy()
p.mtry_params[:, :, 5] = mtry_params_old[:, :, 6]
p.mtry_params[:, :, 6] = mtry_params_old[:, :, 11]
p.mtry_params[:, :, 7] = mtry_params_old[:, :, 5]
p.mtry_params[:, :, 8] = mtry_params_old[:, :, 7]
p.mtry_params[:, :, 9] = mtry_params_old[:, :, 8]
p.mtry_params[:, :, 10] = mtry_params_old[:, :, 9]
p.mtry_params[:, :, 11] = mtry_params_old[:, :, 10]
p.lambdas = lambdas.reshape(p.J, 1)
p.num_workers = 1
bq = BQ / p.lambdas[j]
tr = TR
test_list = TPI.firstdoughnutring(guesses, r, w, bq, tr, theta, factor, j,
initial_b, p)
expected_list = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data',
'firstdoughnutring_outputs.pkl'))
assert (np.allclose(np.array(test_list), np.array(expected_list)))
ids=['scalar', 'vector', 'matrix', '3D array'])
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.J = 1
p1.beta = np.ones(p1.J) * 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.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]]),
def test_inner_loop(dask_client):
# Test TPI.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', 'tpi_inner_loop_inputs.pkl'))
guesses, outer_loop_vars, params, j = input_tuple
income_tax_params, tpi_params, initial_values, ind = params
initial_values = initial_values[:-1]
tpi_params = tpi_params
p = Specifications(client=dask_client, num_workers=NUM_WORKERS)
(p.J, p.S, p.T, p.BW, 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.beta = np.ones(p.J) * beta
p.eta = p.omega.reshape(p.T + p.S, p.S, 1) * p.lambdas.reshape(1, 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.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.tax_func_type = 'DEP'
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, :, :]
etr_params_old = p.etr_params
mtrx_params_old = p.mtrx_params
mtry_params_old = p.mtry_params
p.etr_params = etr_params_old.copy()
p.etr_params[:, :, 5] = etr_params_old[:, :, 6]
p.etr_params[:, :, 6] = etr_params_old[:, :, 11]
p.etr_params[:, :, 7] = etr_params_old[:, :, 5]
p.etr_params[:, :, 8] = etr_params_old[:, :, 7]
p.etr_params[:, :, 9] = etr_params_old[:, :, 8]
p.etr_params[:, :, 10] = etr_params_old[:, :, 9]
p.etr_params[:, :, 11] = etr_params_old[:, :, 10]
p.mtrx_params = mtrx_params_old.copy()
p.mtrx_params[:, :, 5] = mtrx_params_old[:, :, 6]
p.mtrx_params[:, :, 6] = mtrx_params_old[:, :, 11]
p.mtrx_params[:, :, 7] = mtrx_params_old[:, :, 5]
p.mtrx_params[:, :, 8] = mtrx_params_old[:, :, 7]
p.mtrx_params[:, :, 9] = mtrx_params_old[:, :, 8]
p.mtrx_params[:, :, 10] = mtrx_params_old[:, :, 9]
p.mtrx_params[:, :, 11] = mtrx_params_old[:, :, 10]
p.mtry_params = mtry_params_old.copy()
p.mtry_params[:, :, 5] = mtry_params_old[:, :, 6]
p.mtry_params[:, :, 6] = mtry_params_old[:, :, 11]
p.mtry_params[:, :, 7] = mtry_params_old[:, :, 5]
p.mtry_params[:, :, 8] = mtry_params_old[:, :, 7]
p.mtry_params[:, :, 9] = mtry_params_old[:, :, 8]
p.mtry_params[:, :, 10] = mtry_params_old[:, :, 9]
p.mtry_params[:, :, 11] = mtry_params_old[:, :, 10]
p.lambdas = lambdas.reshape(p.J, 1)
p.num_workers = 1
(K0, b_sinit, b_splus1init, factor, initial_b, initial_n, p.omega_S_preTP,
initial_debt) = initial_values
initial_values_in = (K0, b_sinit, b_splus1init, factor, initial_b,
initial_n)
(r, K, BQ, TR) = outer_loop_vars
wss = firm.get_w_from_r(r[-1], p, 'SS')
w = np.ones(p.T + p.S) * wss
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
outer_loop_vars_in = (r, w, r, BQ, TR, theta)
guesses = (guesses[0], guesses[1])
test_tuple = TPI.inner_loop(guesses, outer_loop_vars_in, initial_values_in,
j, ind, p)
expected_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'tpi_inner_loop_outputs.pkl'))
for i, v in enumerate(expected_tuple):
assert (np.allclose(test_tuple[i], v))
