Random Effects Models

László Balázsi; Badi H. Baltagi; László Mátyás; Daria Pus

doi:10.1007/978-3-031-49849-7_3

Random Effects Models

László Balázsi, Badi H. Baltagi, László Mátyás, Daria Pus

Department of Economics

Research output: Chapter in Book/Entry/Poem › Chapter

1 Scopus citations

Abstract

This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics. Due to the many (semi-)asymptotic cases, special attention is given to checking under which cases the presented estimators are consistent. Interestingly, some asymptotic cases also carry a ‘convergence’ property, that is the respective (F)GLS estimator converges to the Within estimator, carrying over some of its identification issues. The results are extended to incomplete panels and to higher dimensions as well. Lastly, mixed fixed–random effects models are visited, and some insights on testing for model specifications are considered.

Original language	English (US)
Title of host publication	Advanced Studies in Theoretical and Applied Econometrics
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	61-98
Number of pages	38
DOIs	https://doi.org/10.1007/978-3-031-49849-7_3
State	Published - 2024

Publication series

Name	Advanced Studies in Theoretical and Applied Econometrics
Volume	54
ISSN (Print)	1570-5811
ISSN (Electronic)	2214-7977

ASJC Scopus subject areas

Economics and Econometrics

Access to Document

10.1007/978-3-031-49849-7_3

Cite this

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abstract = "This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics. Due to the many (semi-)asymptotic cases, special attention is given to checking under which cases the presented estimators are consistent. Interestingly, some asymptotic cases also carry a {\textquoteleft}convergence{\textquoteright} property, that is the respective (F)GLS estimator converges to the Within estimator, carrying over some of its identification issues. The results are extended to incomplete panels and to higher dimensions as well. Lastly, mixed fixed–random effects models are visited, and some insights on testing for model specifications are considered.",

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AU - Baltagi, Badi H.

AU - Mátyás, László

AU - Pus, Daria

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M3 - Chapter

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ER -

Random Effects Models

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