Resource title

Econometric computing with HC and HAC covariance matrix estimators

Resource image

image for OpenScout resource :: Econometric computing with HC and HAC covariance matrix estimators

Resource description

Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity-consistent (HC) and heteroskedasticity and autocorrelation consistent (HAC) estimators have been receiving attention in the econometric literature over the last 20 years. To apply these estimators in practice, an implementation is needed that preferably translates the conceptual properties of the underlying theoretical frameworks into computational tools. In this paper, such an implementation in the package sandwich in the R system for statistical computing is described and it is shown how the suggested functions provide reusable components that build on readily existing functionality and how they can be integrated easily into new inferential procedures or applications. The toolbox contained in sandwich is extremely flexible and comprehensive, including specific functions for the most important HC and HAC estimators from the econometric literature. Several real-world data sets are used to illustrate how the functionality can be integrated into applications. (author's abstract) ; Series: Research Report Series / Department of Statistics and Mathematics

Resource author

Achim Zeileis

Resource publisher

Resource publish date

Resource language

en

Resource content type

application/pdf

Resource resource URL

http://epub.wu.ac.at/520/1/document.pdf

Resource license

Adapt according to the license agreement. Always reference the original source and author.