@inbook{8c4606750807497683b4b3e751d41b80,
title = "Random Effects Models",
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.",
author = "L{\'a}szl{\'o} Bal{\'a}zsi and Baltagi, {Badi H.} and L{\'a}szl{\'o} M{\'a}ty{\'a}s and Daria Pus",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2024.",
year = "2024",
month = jan,
day = "1",
doi = "10.1007/978-3-031-49849-7_3",
language = "English",
isbn = "978-3-031-49851-0",
series = "Advanced Studies in Theoretical and Applied Econometrics",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "61--98",
editor = "Laszlo Matyas",
booktitle = "The Econometrics of Multi-dimensional Panels",
}