def __init__(self, p,
estimate_tax_functions=False, estimate_beta=False,
estimate_chi_n=False,
tax_func_path=None, iit_reform={}, guid='', data='cps',
client=None, num_workers=1):
self.estimate_tax_functions = estimate_tax_functions
self.estimate_beta = estimate_beta
self.estimate_chi_n = estimate_chi_n
if estimate_tax_functions:
self.tax_function_params = self.get_tax_function_parameters(
self, p, iit_reform, guid, data, client, num_workers,
run_micro=True, tax_func_path=tax_func_path)
if estimate_beta:
self.beta_j = estimate_beta_j.beta_estimate(self)
# if estimate_chi_n:
# chi_n = self.get_chi_n()
# Macro estimation
self.macro_params = macro_params.get_macro_params()
# eta estimation
self.eta = transfer_distribution.get_transfer_matrix()
# zeta estimation
self.zeta = bequest_transmission.get_bequest_matrix()
# earnings profiles
self.e = income.get_e_interp(
p.S, p.omega_SS, p.omega_SS_80, p.lambdas,
plot=False)
# demographics
self.demographic_params = demographics.get_pop_objs(
p.E, p.S, p.T, 1, 100, p.start_year)
def test_get_pop_objs():
"""
Test of the that omega_SS and the last period of omega_path_S are
close to each other.
"""
E = 20
S = 80
T = int(round(4.0 * S))
start_year = 2019
pop_dict = demographics.get_pop_objs(E, S, T, 1, 100, start_year, False)
assert np.allclose(pop_dict["omega_SS"], pop_dict["omega"][-1, :])
def test_imm_smooth():
"""
Test that population growth rates evolve smoothly.
"""
E = 20
S = 80
T = int(round(4.0 * S))
start_year = 2019
pop_dict = demographics.get_pop_objs(E, S, T, 1, 100, start_year, False)
assert np.any(
np.absolute(
pop_dict["imm_rates"][:-1, :] - pop_dict["imm_rates"][1:, :]
)
< 0.0001
)