jss_2020.RmdClustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, and other social sciences. They are employed to adjust the inference following estimation of a standard least-squares regression or generalized linear model estimated by maximum likelihood. Although many publications just refer to ``the’’ clustered standard errors, there is a surprisingly wide variety of clustered covariances, particularly due to different flavors of bias corrections. Furthermore, while the linear regression model is certainly the most important application case, the same strategies can be employed in more general models (e.g., for zero-inflated, censored, or limited responses).
In R, functions for covariances in clustered or panel models have
been somewhat scattered or available only for certain modeling
functions, notably the (generalized) linear regression model. In
contrast, an object-oriented approach to “robust” covariance matrix
estimation - applicable beyond lm() and glm()
- is available in the sandwich package but has been limited to
the case of cross-section or time series data. Starting with
sandwich 2.4.0, this shortcoming has been corrected: Based on
methods for two generic functions (estfun() and
bread()), clustered and panel covariances are provided in
vcovCL(), vcovPL(), and vcovPC().
Moreover, clustered bootstrap covariances are provided in
vcovBS(), using model update() on bootstrap
samples. These are directly applicable to models from packages including
MASS, pscl, countreg, and betareg,
among many others. Some empirical illustrations are provided as well as
an assessment of the methods’ performance in a simulation study.
PDF: sandwich-CL.pdf
Originally published as: Zeileis A, Köll S, Graham N (2020). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software, 95(1), 1-36. doi:10.18637/jss.v095.i01.