Feols.vcov

vcov(vcov, vcov_kwargs=None, data=None)

Compute covariance matrices for an estimated regression model.

Parameters

Name	Type	Description	Default
vcov	Union[str, dict[str, str]]	A string or dictionary specifying the type of variance-covariance matrix to use for inference. If a string, it can be one of “iid”, “hetero”, “HC1”, “HC2”, “HC3”, “NW”, “DK”. If a dictionary, it should have the format {“CRV1”: “clustervar”} for CRV1 inference or {“CRV3”: “clustervar”} for CRV3 inference. Note that CRV3 inference is currently not supported for IV estimation.	required
vcov_kwargs	Optional[dict[str, any]]	Additional keyword arguments for the variance-covariance matrix.	None
data	DataFrameType | None	The data used for estimation. If None, tries to fetch the data from the model object. Defaults to None.	None

Returns

Name	Type	Description
	Feols	An instance of class Feols with updated inference.

Examples

Updates the variance estimator of a fitted model without refitting it. The model is modified in place and returned.

import pyfixest as pf

fit = pf.feols("Y ~ X1 + X2 | f1", pf.get_data())
fit.vcov("iid").tidy()

	Estimate	Std. Error	t value	Pr(>|t|)	2.5%	97.5%
Coefficient
X1	-0.949526	0.066373	-14.305943	0.0	-1.079777	-0.819274
X2	-0.174225	0.017596	-9.901590	0.0	-0.208755	-0.139695

# switch to cluster-robust inference
fit.vcov({"CRV1": "f1"}).tidy()

	Estimate	Std. Error	t value	Pr(>|t|)	2.5%	97.5%
Coefficient
X1	-0.949526	0.066557	-14.266314	1.221245e-14	-1.085650	-0.813401
X2	-0.174225	0.018409	-9.464130	2.267890e-10	-0.211876	-0.136575

See On Small Sample Corrections for how the ssc adjustments interact with each estimator.