Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation

Roger Koenker; José A. F. Machado; Christopher L. Skeels; A. H. Welsh

doi:10.1017/s0266466600008288

Abstract

This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order O p ( n −3/2 ) are employed to construct O ( n −2 ) expansions for the variance of estimators constructed from preliminary least-squares and general M -estimators. In the former case, there is a rather curious robustifying effect due to estimation of the Eicker-White covariance matrix for error distributions with sufficiently large kurtosis.

Keywords

MathematicsEstimatorKurtosisRobustness (evolution)StatisticsCovariance matrixMoment (physics)Minimum-variance unbiased estimatorCovarianceApplied mathematicsMinimum distanceM-estimator

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Publication Info

Year: 1994
Type: article
Volume: 10
Issue: 1
Pages: 172-197
Citations: 31
Access: Closed

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APA Style

                            
                                
                                    Roger Koenker, 
                                
                                    José A. F. Machado, 
                                
                                    Christopher L. Skeels
                                
                                et al.
                            
                            (1994). 
                            Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation. 
                            Econometric Theory
                            , 10
                            (1)
                            , 172-197.
                            https://doi.org/10.1017/s0266466600008288
                        

Identifiers

DOI: 10.1017/s0266466600008288