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Definition
Random effects regression is suited for longitudinal or panel data. The availability of repeated observations on the same units allows the researcher to enrich the model by inserting an additional term in the regression, capturing individual-specific, time-invariant factors affecting the dependent variable but unobserved to the econometrician. Generalized Least Squares estimators of the parameters of such a model are more efficient than those obtained in the simpler model neglecting these unobserved factors.
Description
The random effects (RE, henceforth) regression model is used both in econometrics, where this model is often opposed to the fixed effects (FE, henceforth) regression model (see below), and in statistics, where this model is a special case of multilevel models. The common context of application of both literatures is the one of longitudinal (or panel) data, i.e., data where several subjects...
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References
Arellano, M. (2003). Panel data econometrics. Oxford: Oxford University Press.
Baltagi, B. H. (2008). Econometric analysis of panel data (4th ed.). New York: Wiley.
Hsiao, C. (2003). Analysis of panel data (2nd ed.). Cambridge: Cambridge University Press.
Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46, 69–86.
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Benfratello, L. (2014). Random Effects Regression for Panel Data. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2402
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DOI: https://doi.org/10.1007/978-94-007-0753-5_2402
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