Description
Robust Covariance Matrix Estimators.
Description
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
README.md
Robust Covariance Matrix Estimators
Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data. Modular object-oriented implementation with support for many model objects, including: lm, glm, fixest, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and beyond.
Sandwich covariances for general parametric models:
Object-oriented implementation in R:
library("sandwich")
library("lmtest")
data("PetersenCL", package = "sandwich")
m <- lm(y ~ x, data = PetersenCL)
coeftest(m, vcov = sandwich)
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0297 0.0284 1.05 0.3
## x 1.0348 0.0284 36.45 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
coeftest(m, vcov = vcovCL, cluster = ~ firm)
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0297 0.0670 0.44 0.66
## x 1.0348 0.0506 20.45 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1