问Python statsmodel健壮的cov_type='hac-panel‘问题

EN

import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from pandas.tseries.offsets import *

# Just grabbing some random data here
dat = sm.datasets.macrodata.load_pandas().data
dat['time'] = dat['year'].apply(lambda x: pd.to_datetime(x, format='%Y'))
dat['time'] = dat.apply(lambda x:(x['time'] + BQuarterBegin(x['quarter'])), axis=1)
dat = dat.set_index('time')
dat = dat.sort_index()
dat['dGDP'] = (dat['realgdp'] - dat['realgdp'].shift(1))/dat['realgdp'].shift(1) * 100.0
dat['dM1'] = (dat['m1'] - dat['m1'].shift(1))/dat['m1'].shift(1) * 100.0
dat['dUEMP'] = dat['unemp'] - dat['unemp'].shift(1)
dat['dCPI'] = dat['infl'] - dat['infl'].shift(1)
dat = dat[['dGDP', 'dM1', 'dUEMP', 'dCPI']]

# Fitting the model
y_var = dat.unstack()
x_var = pd.DataFrame(dat.shift(1).unstack(), columns=['01m']).combine_first(pd.DataFrame(dat.shift(3).unstack(), columns=['03m'])).combine_first(pd.DataFrame(dat.shift(12).unstack(), columns=['12m']))

model = sm.OLS(y_var, sm.add_constant(x_var), missing='drop')

res = model.fit(cov_type='hac-panel', cov_kwds={'time': dat.index, 'maxlags': 11})
res.summary()

model.fit(cov_type='hac-panel', cov_kwds={'time': dat.index, 'groups': dat.index, 'maxlags': 11})

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-74b3e662267b> in <module>
----> 1 model.fit(cov_type='hac-panel', cov_kwds={'time': dat.index, 'groups': dat.index, 'maxlags': 11})

~\anaconda3\lib\site-packages\statsmodels\regression\linear_model.py in fit(self, method, cov_type, cov_kwds, use_t, **kwargs)
    343                 self, beta,
    344                 normalized_cov_params=self.normalized_cov_params,
--> 345                 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t)
    346         else:
    347             lfit = RegressionResults(

~\anaconda3\lib\site-packages\statsmodels\regression\linear_model.py in __init__(self, model, params, normalized_cov_params, scale, cov_type, cov_kwds, use_t, **kwargs)
   1555                 # TODO: warn or not?
   1556             self.get_robustcov_results(cov_type=cov_type, use_self=True,
-> 1557                                        use_t=use_t, **cov_kwds)
   1558         for key in kwargs:
   1559             setattr(self, key, kwargs[key])

~\anaconda3\lib\site-packages\statsmodels\regression\linear_model.py in get_robustcov_results(self, cov_type, use_t, **kwargs)
   2490             res.cov_params_default = sw.cov_nw_panel(self, maxlags, groupidx,
   2491                                                      weights_func=weights_func,
-> 2492                                                      use_correction=use_correction)
   2493             res.cov_kwds['description'] = descriptions['HAC-Panel']
   2494 

~\anaconda3\lib\site-packages\statsmodels\stats\sandwich_covariance.py in cov_nw_panel(results, nlags, groupidx, weights_func, use_correction)
    785     xu, hessian_inv = _get_sandwich_arrays(results)
    786 
--> 787     S_hac = S_nw_panel(xu, weights, groupidx)
    788     cov_hac = _HCCM2(hessian_inv, S_hac)
    789     if use_correction:

~\anaconda3\lib\site-packages\statsmodels\stats\sandwich_covariance.py in S_nw_panel(xw, weights, groupidx)
    723     S = weights[0] * np.dot(xw.T, xw)  #weights just for completeness
    724     for lag in range(1, nlags+1):
--> 725         xw0, xwlag = lagged_groups(xw, lag, groupidx)
    726         s = np.dot(xw0.T, xwlag)
    727         S += weights[lag] * (s + s.T)

~\anaconda3\lib\site-packages\statsmodels\stats\sandwich_covariance.py in lagged_groups(x, lag, groupidx)
    706 
    707     if out0 == []:
--> 708         raise ValueError('all groups are empty taking lags')
    709     #return out0, out_lagged
    710     return np.vstack(out0), np.vstack(out_lagged)

ValueError: all groups are empty taking lags