Asymptotics for Random Effects Models with Serial Correlation

Posted: 30 Mar 2013

See all articles by Jimmy Skoglund

Jimmy Skoglund

SAS Institute Inc.

Sune Karlsson

University of Orebro - Department of Economics

Date Written: March 28, 2001

Abstract

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models. Consistent estimation and asymptotic normality as N and/or T grows large is established for a comprehensive specification which allows for serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. The consistency and asymptotic normality properties of all commonly used random effects models are obtained as special cases of the comprehensive model. When N or T >infty only a subset of the parameters are consistent and asymptotic normality is established for the consistent subsets.

Keywords: error component, maximum likelihood, asymptotic behavior

Suggested Citation

Skoglund, Jimmy and Karlsson, Sune, Asymptotics for Random Effects Models with Serial Correlation (March 28, 2001). Available at SSRN: https://ssrn.com/abstract=2241033

Jimmy Skoglund (Contact Author)

SAS Institute Inc. ( email )

100 SAS Campus Drive
Cary, NC 27513-2414
United States

Sune Karlsson

University of Orebro - Department of Economics ( email )

SE-70182 Orebro
Sweden
+46 19 301 257 (Phone)

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