Random effects model

• abstract: In statistics, a random effect(s) model, also called a variance components model, is a kind of hierarchical linear model. It assumes that the dataset being analysed consists of a hierarchy of different populations whose differences relate to that hierarchy. In econometrics, random effects models are used in the analysis of hierarchical or panel data when one assumes no fixed effects (i.e. no individual effects). The random effects model is a special case of the fixed effects model. Contrast this to the biostatistics definitions,Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, and Scott L. Zeger (2002) Analysis of Longitudinal Data. 2nd ed., Oxford University Press, p. 169-171.Garrett M. Fitzmaurice, Nan M. Laird, and James H. Ware (2004) Applied Longitudinal Analysis. John Wiley & Sons, Inc., p. 326-328.Nan M. Laird and James H. Ware (1982) "Random-Effects Models for Longitudinal Data," Biometrics, 38 (4), 963-974. as biostatisticians use "fixed" and "random" effects to respectively refer to the population

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