Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R
Dublin Core
Title
Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R
Subject
: clustered data, covariance matrix estimator, object orientation, simulation, R.
Description
Clustered 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
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
Creator
Achim Zeileis
Source
https://www.jstatsoft.org/article/view/v095i01
Publisher
Universität Innsbruck
Date
October 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Achim Zeileis, “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8151.