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Object-oriented Computation of Sandwich Estimators

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  • Achim Zeileis

Abstract

Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 16 ()
Issue (Month): i09 ()
Pages:

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Handle: RePEc:jss:jstsof:16:i09

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  1. Fair, Ray C, 1978. "A Theory of Extramarital Affairs," Journal of Political Economy, University of Chicago Press, vol. 86(1), pages 45-61, February.
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  6. James G. MacKinnon & Halbert White, 1983. "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Working Papers 537, Queen's University, Department of Economics.
  7. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
  8. Achim Zeileis, . "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, American Statistical Association, vol. 11(i10).
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Citations

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Cited by:
  1. Claudio Lupi, . "Unit Root CADF Testing with R," Journal of Statistical Software, American Statistical Association, vol. 32(i02).
  2. Christian Kleiber & Achim Zeileis, 2010. "The Grunfeld Data at 50," German Economic Review, Verein für Socialpolitik, vol. 11, pages 404-417, November.
  3. Paola Brighi & Roberto Patuelli & Giuseppe Torluccio, 2012. "Self-Financing of Traditional and R&D Investments: Evidence from Italian SMEs," Working Paper Series 61_12, The Rimini Centre for Economic Analysis.
  4. Patnaik, Ila & Shah, Ajay, 2010. "Does the currency regime shape unhedged currency exposure?," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 760-769, September.
  5. W. Lee, 2014. "Historical global analysis of occurrences and human casualty of extreme temperature events (ETEs)," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1453-1505, January.
  6. Achim Zeileis & Roger Koenker, . "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, American Statistical Association, vol. 27(i01).
  7. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
  8. Lupi, Claudio, 2009. "Covariate Augmented Dickey-Fuller Tests with R," Economics & Statistics Discussion Papers esdp09051, University of Molise, Dept. EGSeI.
  9. Millo, Giovanni, 2014. "Robust standard error estimators for panel models: a unifying approach," MPRA Paper 54954, University Library of Munich, Germany.
  10. Simon Jackman & Christian Kleiber & Achim Zeileis, 2007. "Regression Models for Count Data in R," Working papers 2007/24, Faculty of Business and Economics - University of Basel.
  11. Sarah Harris & Wendy Anderson & Musa Kilinc & Liam Fogarty, 2012. "The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 391-415, September.
  12. Javier Contreras-Reyes & Wilfredo Palma, 2013. "Statistical analysis of autoregressive fractionally integrated moving average models in R," Computational Statistics, Springer, vol. 28(5), pages 2309-2331, October.

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