A Note on the Efficiency of Sandwich Covariance Matrix Estimation

By Goeran Kauermann, Raymond J. Carroll. 2001

631 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/faa7d049b16ae244cffa5b6859a6a880969a37f3

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Citations in the corpus

Testing Negative Error Variances (2012, methodology): “Carroll, Wang, Simpson, Stromberg & Ruppert (1998) and Kauermann & Carroll (2001) argue that this small sample performance is the price one has to pay for its consistency, as it is typical in robust statistics to compromise efficiency for robustness.”
Tapered Covariance: Bayesian Estimation and Asymptotics (2012, background): “…study a sandwich estimator of the sampling covariance matrix (e.g., Durbin 1960; Bhapkar 1972; Morton 1981; Ferreira 1982; Godambe and Heyde 1987; Kauermann and Carroll 2001), and mentioned that, under conditions that are not met in the context of Gaussian random fields, quasi-likelihood theory…”
Nonconcave penalized likelihood with a diverging number of parameters (2004, methodology): “For a review of sandwich covariance matrix estimation, see the paper of Kauermann and Carroll (2001).”
Inference in Linear Regression Models with Many Covariates and Heteroscedasticity (2015, background): “…(White 1980; MacKinnon and White 1985; Wu 1986; Chesher and Jewitt 1987; Shao and Wu 1987; Chesher 1989; Cribari-Neto, Ferrari, and Cordeiro 2000; Kauermann and Carroll 2001; Bera, Suprayitno, and Premaratne 2002; Stock and Watson 2008; Cribari-Neto, da Gloria, and Lima 2011; Müller 2013;…”
The American Identity Measure: Development and Validation across Ethnic Group and Immigrant Generation (2012, methodology): “05 for any of the study variables, we used the sandwich covariance estimator (Kauermann & Carroll, 2001 ), which adjusts the standard errors of model parameters to account for multilevel nesting but does not explicitly model level 2 (site) effects.”
Resurrecting weighted least squares (2017, neutral): “These facts have been demonstrated by Kauermann and Carroll (2001) analytically and by Long and Ervin (2000), Kauermann and Carroll (2001), Cribari-Neto (2004), and Angrist and Pischke (2009, Section 8.”
Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016, methodology): “…(12 in this example), a KenwardRoger correction (Kenward & Roger, 1997) was used with the multilevel model and a Kauermann–Carroll correction (Kauermann & Carroll, 2001) was applied to the clusterrobust estimator for the single-level model; both of these corrections have been advocated in…”
Gaussian Copula Marginal Regression (2012, methodology): “This result illustrates the well-known very slow convergence of robust variance estimation using sandwich-type methods; see Kauermann and Carroll (2001) for a general discussion on this point.”
Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments (2014, neutral): “But, correctly specifying “working” correlation structure can definitely enhance the efficiency of the parameter estimates in particular when the sample size is not large enough [16, 24, 25, 29].”
Robust Inference with Clustered Data (2010, background): “Kauermann and Carroll (2001), Bell and McCa¤rey (2002), Mancl and DeRouen (2001), and McCa¤rey, Bell and Botts (2001) also consider the case where g 6= 2I is of known functional form, and present extension to generalized linear models.”