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Econometric Computing with HC and HAC Covariance Matrix Estimators
Open accessAbstract
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.
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Cites
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maars: Tidy Inference under the 'Models as Approximations' Framework in R
Fogliato, R., Shrotriya, S., & Kuchibhotla, A. K. (2021) · arXiv · doi:10.48550/arxiv.2106.11188
Metadata
- Authors
- Zeileis, A.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2004
- Volume
- 11
- Issue
- 10
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2136362180
- Cited by
- 1,285
- References
- 1
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- diamond
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