create new results instance with robust covariance as default
Parameters: | cov_type : string
use_t : bool
kwds : depends on cov_type
|
---|---|
Returns: | results : results instance
|
Notes
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 use
kernel string (optional) : kernel, default is Bartlett
- use_correction bool (optional) : If true, use small sample
correction
‘cluster’ and required keyword groups, integer group indicator
- groups array_like, integer (required) :
index of clusters or groups
- use_correction bool (optional) :
If True the sandwich covariance is calculated with a small sample correction. If False the sandwich covariance is calculated without small sample correction.
- df_correction bool (optional)
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, 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 periods
maxlag integer (required) : number of lags to use
kernel string (optional) : kernel, default is Bartlett
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.
adjustment to df_resid, see cov_type ‘cluster’ above #TODO: we need more options here
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
either groups or time : array_like (required) groups : indicator for groups time : index of time periods
maxlag integer (required) : number of lags to use
kernel string (optional) : kernel, default is Bartlett
If False the sandwich covariance is calculated without small sample correction.
adjustment to df_resid, see cov_type ‘cluster’ above #TODO: we need more options here
Reminder: use_correction in “hac-groupsum” and “hac-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