def _full_solution(wages, nonpecs, continuation_values, period_draws_emax_risk,
optim_paras):
"""Calculate the full solution of the model.
In contrast to approximate solution, the Monte Carlo integration is done for each
state and not only a subset of states.
"""
period_expected_value_functions = calculate_expected_value_functions(
wages,
nonpecs,
continuation_values,
period_draws_emax_risk,
optim_paras["delta"],
)
return period_expected_value_functions
def _compute_lhs_variable(
wages,
nonpec,
continuation_values,
max_value_functions,
not_interpolated,
draws,
delta,
):
"""Calculate left-hand side variable for all states which are not interpolated.
The function computes the full solution for a subset of states. Then, the dependent
variable is the expected value function minus the maximum of value function with the
expected shocks.
Parameters
----------
wages : numpy.ndarray
Array with shape (n_states_in_period, n_choices).
nonpec : numpy.ndarray
Array with shape (n_states_in_period, n_choices).
continuation_values : numpy.ndarray
Array with shape (n_states_in_period, n_choices).
max_value_functions : numpy.ndarray
Array with shape (n_states_in_period,) containing maximum over all value
functions computed with the expected value of shocks.
not_interpolated : numpy.ndarray
Array with shape (n_states_in_period,) containing indicators for simulated
continuation_values.
draws : numpy.ndarray
Array with shape (n_draws, n_choices) containing draws.
delta : float
Discount factor.
"""
expected_value_functions = calculate_expected_value_functions(
wages[not_interpolated],
nonpec[not_interpolated],
continuation_values[not_interpolated],
draws,
delta,
)
endogenous = expected_value_functions - max_value_functions[
not_interpolated]
return endogenous
from respy.shared import calculate_expected_value_functions
import numpy as np
import pandas as pd
# Set up inputs
wage = np.array([1.46178695e04, 9.70115277e03, 1.00000000e00, 1.00000000e00])
nonpec = np.array([0.0, 0.0, -4000.0, 17750.0])
continuation_values = np.array(
[359856.6202004, 362415.98557173, 375897.29303581, 353287.24408844])
draws = pd.read_pickle("draws.pickle")
delta = 0.95
# Evaluate the integral
res = calculate_expected_value_functions(wage, nonpec, continuation_values,
draws, delta)
print(res)