Random Effects Models

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

    Research output: Contribution to Book/Report typesChapterpeer-review

    Abstract (may include machine translation)

    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 languageEnglish
    Title of host publicationThe Econometrics of Multi-dimensional Panels
    EditorsLaszlo Matyas
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages61-98
    Number of pages38
    ISBN (Print)978-3-031-49851-0
    DOIs
    StatePublished - 1 Jan 2024

    Publication series

    NameAdvanced Studies in Theoretical and Applied Econometrics
    Volume54
    ISSN (Print)1570-5811
    ISSN (Electronic)2214-7977

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