OLSResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds)
create new results instance with robust covariance as default
Parameters: 


Returns: 
results – This method creates a new results instance with the requested robust covariance as the default covariance of the parameters. Inferential statistics like pvalues and hypothesis tests will be based on this covariance matrix. 
Return type: 
results instance 
The following covariance types and required or optional arguments are currently available:
a predefined scale estimate with default equal to one.
heteroscedasticity robust covariance
‘HAC’ and keywords
maxlag
integer (required) : number of lags to usekernel callable or str (optional) : kernel
use_correction bool (optional) : If true, use small sample
‘cluster’ and required keyword groups
, integer group indicator
groups array_like, integer (required) :
use_correction bool (optional) :
df_correction bool (optional)
df_resid
of the results instance is adjusted. If False, then df_resid
of the results instance is not adjusted.autocorrelation robust standard errors in panel data keywords
time
array_like (required) : index of time periodsmaxlag
integer (required) : number of lags to usekernel callable or str (optional) : kernel
use_correction False or string in [‘hac’, ‘cluster’] (optional) :
use_correction = ‘cluster’
(default), then the same small sample correction as in the case of ‘covtype=’cluster’’ is used.df_correction bool (optional)
errors in panel data. The data needs to be sorted in this case, the time series for each panel unit or cluster need to be stacked. The membership to a timeseries of an individual or group can be either specified by group indicators or by increasing time periods.
keywords
groups
or time
: array_like (required) groups
: indicator for groups time
: index of time periodsmaxlag
integer (required) : number of lags to usekernel callable or str (optional) : kernel
use_correction False or string in [‘hac’, ‘cluster’] (optional) :
df_correction bool (optional)
Reminder: use_correction
in “hacgroupsum” and “hacpanel” is not bool, needs to be in [False, ‘hac’, ‘cluster’]
TODO: Currently there is no check for extra or misspelled keywords, except in the case of cov_type HCx
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.get_robustcov_results.html