Error in Fitting Higher Lag AR Models for Prices
Getting the following error please help
Definition of Function:
def LLR_test(mod_1, mod_2, DF=1):
L1 = mod_1.fit().llf
L2 = mod_2.fit().llf
LR = (2*(L2-L1))
p = chi2.sf(LR, DF).round(3)
return p
LLR_test(model_ar_2, model_ar_3)
Error Message:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-21-be745ec553e3> in <module>() ----> 1 LLR_test(model_ar_2, model_ar_3)3 frames
<ipython-input-20-3e315fe52fa6> in LLR_test(mod_1, mod_2, DF) 1 def LLR_test(mod_1, mod_2, DF=1): ----> 2 L1 = mod_1.fit().llf 3 L2 = mod_2.fit().llf 4 LR = (2*(L2-L1)) 5 p = chi2.sf(LR, DF).round(3)
/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/arima_model.py in fit(self, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, start_ar_lags, **kwargs) 902 # (re)set trend and handle exogenous variables 903 # always pass original exog --> 904 k_trend, exog = _make_arma_exog(endog, self.exog, trend) 905 906 # Check has something to estimate
/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/arima_model.py in _make_arma_exog(endog, exog, trend) 395 exog = np.ones((len(endog), 1)) 396 elif exog is not None and trend == 'c': # constant plus exogenous --> 397 exog = add_trend(exog, trend='c', prepend=True, has_constant='raise') 398 elif exog is not None and trend == 'nc': 399 # make sure it's not holding constant from last run
/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/tsatools.py in add_trend(x, trend, prepend, has_constant) 106 msg = "x contains a constant. Adding a constant with " \ 107 "trend='{0}' is not allowed.".format(trend) --> 108 raise ValueError(msg) 109 elif has_constant == 'skip': 110 columns = columns[1:]
ValueError: x contains a constant. Adding a constant with trend='c' is not allowed.
1 answers ( 0 marked as helpful)
Hey Devansh,
This question has already been answered in the Q&A Hub. https://365datascience.com/question/comparing-higher-lag-ar-models/
Best,
365 Vik
This question has already been answered in the Q&A Hub. https://365datascience.com/question/comparing-higher-lag-ar-models/
Best,
365 Vik