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A1621
Title: Various versatile variances: An object-oriented implementation of clustered covariances in R Authors:  Susanne Berger - University of Innsbruck (Austria) [presenting]
Nathaniel Graham - Trinity University (United States)
Achim Zeileis - Universitaet Innsbruck (Austria)
Abstract: Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, or 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 variation in 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). We discuss an object-oriented implementation based on the building blocks provided by R package ``sandwich'' and assess its performance in a simulation study.