Empirical standard errors for longitudinal data mixed linear models
type: Item
instance of: scholarly article
title: Empirical standard errors for longitudinal data mixed linear models
zbMATH Open document ID: 1062.62118
DOI: 10.1007/BF03372107
author: Edward W. Frees
author: Chunfang Jin
published in: Computational Statistics
publication date: 2005-05-20T00:00:00Z
review text: The model under consideration is a special case of linear regression with independence among subjects. Such estimators as empirical covariation estimators and empirical standard errors (ESE) are widely used in statistical inferences connected with the mentioned regression models. The authors extend work of \textit{J. G. MacKinnon} and \textit{H. White} [J. Econom. 29, 53--57 (1985)] to the longitudinal data mixed linear models by considering alternative approaches to estimation of regression coefficient standard errors. The first type of the proposed estimators is jackknife, the second one is based on standardized residuals. The residual based estimators occur to be unbiased in many cases. Monte Carlo simulation experiments demonstrated that both alternatives have better small sample properties in hypothesis testing than the usual ESE, regardless whether heteroscedasticity is present. Tests based on both alternatives behave well in terms of size and power even when the covariance matrix is completely misspecified.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62F40
zbMATH DE Number: 2169420
zbMATH Keywords: generalized linear models
zbMATH Keywords: mixed linear models
zbMATH Keywords: jackknife estimator
zbMATH Keywords: residuals
zbMATH Keywords: panel data models
zbMATH Keywords: heteroscedasticity
zbMATH Keywords: sandwich estimators
zbMATH Keywords: small sample properties
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 2169420