Random effects models

Laszlo Balazsi, Badi H Baltagi, Laszlo Matyas, Daria Pus

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike in 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 languageEnglish (US)
Title of host publicationAdvanced Studies in Theoretical and Applied Econometrics
PublisherSpringer
Pages35-69
Number of pages35
DOIs
StatePublished - Jan 1 2017

Publication series

NameAdvanced Studies in Theoretical and Applied Econometrics
Volume50
ISSN (Print)1570-5811
ISSN (Electronic)2214-7977

ASJC Scopus subject areas

  • Economics and Econometrics

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  • Cite this

    Balazsi, L., Baltagi, B. H., Matyas, L., & Pus, D. (2017). Random effects models. In Advanced Studies in Theoretical and Applied Econometrics (pp. 35-69). (Advanced Studies in Theoretical and Applied Econometrics; Vol. 50). Springer. https://doi.org/10.1007/978-3-319-60783-2_2