Econometric Computing with HC and HAC Covariance Matrix Estimators

By Achim Zeileis. 2004

1,180 citations recorded by the source corpus.

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.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c319ebae1bc16bc880880940f08d7cabcc1600f0

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Citations in the corpus

Zelig: Everyone's Statistical Software (2006, methodology): “If TRUE is selected, zelig() computes robust standard errors via the sandwich package (see [63]).”
Regression Models for Count Data in R (2008, methodology): “In R , this estimator is provided by the sandwich() function in the sandwich package (Zeileis 2004, 2006).”
Beta Regression in R (2010, methodology): “A set of standard extractor functions for ﬁtted model objects is available for objects of class “ betareg ”, including the usual summary() as discussed by Zeileis (2004, 2006b) as well as a method to a new generic for computing generalized leverages (Wei et al. 1998).”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, methodology): “In these cases, we use the automatic bandwidth selection procedure of Newey and West (1994) as implemented in R’s sandwich package (Zeileis 2004).”
Resurrecting weighted least squares (2017, neutral): “…for example, see MacKinnon and White (1985), Long and Ervin (2000), Angrist and Pischke (2009, Section 8.1), and MacKinnon (2012).9 It is also the default option in several statistical software packages to carry out HC estimation, such as in the R function vcov(); for example, see Zeileis (2004).”
The link between supply chain fit and financial performance of the firm (2012, methodology): “Nevertheless, to correct for possible heteroskedasticity and obtain correct standard error estimates, we used the Huber–White correction (Huber, 1967; White, 1980) implemented in the package sandwich in R (Zeileis, 2004).”
The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy (2014, methodology): “To control for our finite sample size, we performed 10 000 boostrapped estimations, correcting for correlated noise with the standard HAC method [28].”
Homophony and morphology: The acoustics of word-final S in English 1 (2015, methodology): “…the same word type may also arise from more general processes of [5] For the comparisons the packages multcomp (Hothorn et al. 2008) and sandwich (Zeileis 2004, 2006) were used, implementing the specific procedure suggested by Herberich et al. (2010) for scenarios with unbalanced group sizes,…”
Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods (2009, background): “sandwich ( Zeileis 2004 ) pava.blocks() internal isotonic regression, weights R”
ProbABEL package for genome-wide association analysis of imputed data (2010, methodology): “For computations of robust variance-covariance matrix we use White ’ s sandwich estimator [17,18], which is equivalent to the “ HC0 ” estimator described by Zeilers and Lumley in “ sandwich “ package for R.”
Multilevel Modeling and Ordinary Least Squares Regression: How Comparable Are They? (2018, methodology): “As these models were being tested for their potential to reduce bias, the OLS regression used cluster robust standard errors to account for the nesting using the sandwich (Zeleis, 2004) package based on specifications of Arai (2015).”
Object-oriented Computation of Sandwich Estimators (2006, methodology): “Zeileis (2004) discusses a set of computational tools provided by the sandwich package for the R system for statistical computing ( R Development Core Team 2006) which allows for computing HC and HAC estimators in linear regression models ﬁtted by lm() .”
Applied Linear Regression (2004, background): “Corrections for different types of variance misspecification, such as clustering or autoregressive errors, are described by Long and Ervin (2000) and Zeileis (2004).”
Simultaneous Inference in General Parametric Models (2008, neutral): “…to compute some alternative covariance estimator S n as follows: R> amod_glht_sw - glht(amod, linfct = mcp(alength = "Tukey") We used the sandwich() function from package sandwich (Zeileis, 2004, 2006) which provides us with a heteroscedasticity-consistent estimator of the covariance matrix.”
Panel data econometrics in R: The plm package (2008, methodology): “An established solution exists for the random eﬀects model which reduces the problem to an ordinary least squares computation.”