statsmodels.regression.linear_model.OLSResults.get_robustcov_results
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OLSResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds)
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create new results instance with robust covariance as default
Parameters: cov_type : string
the type of robust sandwich estimator to use. see Notes below
use_t : bool
If true, then the t distribution is used for inference. If false, then the normal distribution is used.
kwds : depends on cov_type
Required or optional arguments for robust covariance calculation. see Notes below
Returns: results : results instance
This method creates a new results instance with the requested robust covariance as the default covariance of the parameters. Inferential statistics like p-values and hypothesis tests will be based on this covariance matrix.
Notes
The following covariance types and required or optional arguments are currently available:
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- ?HC0?, ?HC1?, ?HC2?, ?HC3? and no keyword arguments:
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heteroscedasticity robust covariance
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?HAC? and keywords
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maxlag
integer (required) : number of lags to use -
kernel
string (optional) : kernel, default is Bartlett -
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use_correction bool (optional) : If true, use small sample
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correction
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?cluster? and required keyword
groups
, integer group indicator-
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groups array_like, integer (required) :
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index of clusters or groups
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use_correction bool (optional) :
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If True the sandwich covariance is calulated with a small sample correction. If False the the sandwich covariance is calulated without small sample correction.
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df_correction bool (optional)
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If True (default), then the degrees of freedom for the inferential statistics and hypothesis tests, such as pvalues, f_pvalue, conf_int, and t_test and f_test, are based on the number of groups minus one instead of the total number of observations minus the number of explanatory variables.
df_resid
of the results instance is adjusted. If False, thendf_resid
of the results instance is not adjusted.
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- ?hac-groupsum? Driscoll and Kraay, heteroscedasticity and
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autocorrelation robust standard errors in panel data keywords
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time
array_like (required) : index of time periods -
maxlag
integer (required) : number of lags to use -
kernel
string (optional) : kernel, default is Bartlett -
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use_correction False or string in [?hac?, ?cluster?] (optional) :
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If False the the sandwich covariance is calulated without small sample correction. If
use_correction = ?cluster?
(default), then the same small sample correction as in the case of ?covtype=?cluster?? is used.
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df_correction bool (optional)
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adjustment to df_resid, see cov_type ?cluster? above #TODO: we need more options here
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- ?hac-panel? heteroscedasticity and autocorrelation robust standard
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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. keywords
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time
array_like (required) : index of time periods -
maxlag
integer (required) : number of lags to use -
kernel
string (optional) : kernel, default is Bartlett -
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use_correction False or string in [?hac?, ?cluster?] (optional) :
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If False the the sandwich covariance is calulated without small sample correction.
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df_correction bool (optional)
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adjustment to df_resid, see cov_type ?cluster? above #TODO: we need more options here
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Reminder:
use_correction
in ?nw-groupsum? and ?nw-panel? 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
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