"""
Contains values used to approximate the critical value and p-value from DFGLS
statistics

These have been computed using the methodology of MacKinnon (1994) and (2010)
simulation. See dfgls_critival_values_simulation for implementation.
"""
from __future__ import annotations

from numpy import array

dfgls_cv_approx = {
    "c": array(
        [
            [-2.56781793e00, -2.05575392e01, 1.82727674e02, -1.77866664e03],
            [-1.94363325e00, -2.17272746e01, 2.60815068e02, -2.26914916e03],
            [-1.61998241e00, -2.32734708e01, 3.06474378e02, -2.57483557e03],
        ]
    ),
    "ct": array(
        [
            [-3.40689134, -21.69971242, 27.26295939, -816.84404772],
            [-2.84677178, -19.69109364, 84.7664136, -799.40722401],
            [-2.55890707, -19.42621991, 116.53759752, -840.31342847],
        ]
    ),
}

dfgls_tau_max = {"c": 13.365361509140614, "ct": 8.73743383728356}

dfgls_tau_min = {"c": -17.561302895074206, "ct": -13.681153542634465}

dfgls_tau_star = {"c": -0.4795076091714674, "ct": -2.1960404365401298}

dfgls_large_p = {
    "c": [0.50612497, 0.98305664, -0.05648525, 0.00140875],
    "ct": [2.60561421, 1.67850224, 0.0373599, -0.01017936],
}

dfgls_small_p = {
    "c": [0.67422739, 1.25475826, 0.03572509],
    "ct": [2.38767685, 1.57454737, 0.05754439],
}