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dfgls.py
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dfgls.py
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"""
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.
"""
from numpy import array
dfgls_cv_approx = {'c': array([[-2.56781793e+00, -2.05575392e+01, 1.82727674e+02,
-1.77866664e+03],
[-1.94363325e+00, -2.17272746e+01, 2.60815068e+02,
-2.26914916e+03],
[-1.61998241e+00, -2.32734708e+01, 3.06474378e+02,
-2.57483557e+03]]),
'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': array([0.50612497, 0.98305664, -0.05648525, 0.00140875]),
'ct': array([2.60561421, 1.67850224, 0.0373599, -0.01017936])}
dfgls_small_p = {'c': array([0.67422739, 1.25475826, 0.03572509]),
'ct': array([2.38767685, 1.57454737, 0.05754439])}