Bootstrapping heteroskedasticity consistent covariance matrix estimator
type: Item
instance of: scholarly article
title: Bootstrapping heteroskedasticity consistent covariance matrix estimator
zbMATH Open document ID: 1037.62023
author: Emmanuel Flachaire
published in: Computational Statistics
publication date: 2004-03-16T00:00:00Z
review text: This paper demonstrates that the wild bootstrap covariance matrix estimator for linear heteroskedasticity models can be calculated directly without simulation, since it is simply a traditional heteroskedasticity consistent matrix estimator (HCCME) proposed by \textit{F. Eicker} [Ann. Math. Stat. 34, 447--456 (1963; Zbl 0111.34003)] and \textit{H. White} [Econometrica 48, 817--838 (1980; Zbl 0459.62051)]. Both theoretical and experimental results make clear that the same forms of the wild bootstrap and of the HCCME estimator have similar finite sample behaviour, and that the wild always would be preferred to the wild bootstrap obtained by simulation (because a finite number of simulations introduces an experimental error).
Mathematics Subject Classification ID: 62F40
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 2058952
zbMATH Keywords: wild bootstrap
zbMATH Keywords: heteroskedasticity
zbMATH Keywords: covariance matrix
MaRDI profile type: Publication
full work available at URL: s001800200122
OpenAlex ID: W1515304578
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DOI: 10.1007/S001800200122
description: scientific article; zbMATH DE number 2058952