vcov.feglm: Compute covariance matrix after fitting 'feglm'

In alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Compute covariance matrix after fitting feglm

Description

vcov.feglm estimates the covariance matrix for the estimator of the structural parameters from objects returned by feglm. The covariance is computed from the Hessian, the scores, or a combination of both after convergence.

Usage

## S3 method for class 'feglm'
vcov(
  object,
  type = c("hessian", "outer.product", "sandwich", "clustered"),
  cluster = NULL,
  cluster.vars = NULL,
  ...
)

Arguments

object	an object of class "feglm".
type	the type of covariance estimate required. "hessian" refers to the inverse of the negative expected Hessian after convergence and is the default option. "outer.product" is the outer-product-of-the-gradient estimator, "sandwich" is the sandwich estimator (sometimes also referred as robust estimator), and "clustered" computes a clustered covariance matrix given some cluster variables.
cluster	a symbolic description indicating the clustering of observations.
cluster.vars	deprecated; use cluster instead.
...	other arguments.

Details

Multi-way clustering is done using the algorithm of Cameron, Gelbach, and Miller (2011). An example is provided in the vignette "Replicating an Empirical Example of International Trade".

Value

The function vcov.feglm returns a named matrix of covariance estimates.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

See Also

feglm

alpaca documentation built on Aug. 10, 2022, 5:08 p.m.