Random effects model
Regression analysis 

Models 

Estimation 
Background 
This article needs attention from an expert in statistics. (January 2011) 
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 (it allows for individual effects). The random effects model is a special case of the fixed effects model. Contrast this to the biostatistics definitions,^{1}^{2}^{3}^{4} as biostatisticians use "fixed" and "random" effects to respectively refer to the populationaverage and subjectspecific effects (and where the latter are generally assumed to be unknown, latent variables).
Contents
Qualitative description
Such models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and correlated with independent variables. This constant can be removed from the data through differencing, for example by taking a first difference which will remove any time invariant components of the model.
There are two common assumptions made about the individual specific effect, the random effects assumption and the fixed effects assumption. The random effects assumption (made in a random effects model) is that the individual specific effects are uncorrelated with the independent variables. The fixed effect assumption is that the individual specific effect is correlated with the independent variables. If the random effects assumption holds, the random effects model is more efficient than the fixed effects model. However, if this assumption does not hold (i.e., if the Durbin–Watson test fails), the random effects model is not consistent.
Simple example
Suppose m large elementary schools are chosen randomly from among thousands in a large country. Suppose also that n pupils of the same age are chosen randomly at each selected school. Their scores on a standard aptitude test are ascertained. Let Y_{ij} be the score of the jth pupil at the ith school. A simple way to model the relationships of these quantities is
where μ is the average test score for the entire population. In this model U_{i} is the schoolspecific random effect: it measures the difference between the average score at school i and the average score in the entire country and it is "random" because the school has been randomly selected from a larger population of schools. The term, W_{ij} is the individualspecific error. That is, it is the deviation of the jth pupil’s score from the average for the ith school. Again this is regarded as random because of the random selection of pupils within the school, even though it is a fixed quantity for any given pupil.
The model can be augmented by including additional explanatory variables, which would capture differences in scores among different groups. For example:
where Sex_{ij} is the dummy variable for boys/girls, Race_{ij} is the dummy variable for white/black pupils, and ParentsEduc_{ij} records the average education level of child’s parents. This is a mixed model, not a purely random effects model.^{dubious – discuss}
Variance components
The variance of Y_{ij} is the sum of the variances τ^{2} and σ^{2} of U_{i} and W_{ij} respectively.
Let
be the average, not of all scores at the ith school, but of those at the ith school that are included in the random sample. Let
be the "grand average".
Let
be respectively the sum of squares due to differences within groups and the sum of squares due to difference between groups. Then it can be shown that
and
These "expected mean squares" can be used as the basis for estimation of the "variance components" σ^{2} and τ^{2}.
Unbiasedness
In general, random effects is efficient, and should be used (over fixed effects) if the assumptions underlying it are believed to be satisfied. For RE to work in the school example it is necessary that the schoolspecific effects be orthogonal to the other covariates of the model. This can be tested by running fixed effects, then random effects, and doing a Hausman specification test. If the test rejects, then random effects is biased and fixed effects is the correct estimation procedure.
See also
Notes
 ^ Diggle, Peter J.; Heagerty, Patrick; Liang, KungYee; Zeger, Scott L. (2002). Analysis of Longitudinal Data (2nd ed.). Oxford University Press. pp. 169–171. ISBN 0198524846.
 ^ Fitzmaurice, Garrett M.; Laird, Nan M.; Ware, James H. (2004). Applied Longitudinal Analysis. Hoboken: John Wiley & Sons. pp. 326–328. ISBN 0471214876.
 ^ Laird, Nan M.; Ware, James H. (1982). "RandomEffects Models for Longitudinal Data". Biometrics 38 (4): 963–974. JSTOR 2529876.
 ^ Gardiner, Joseph C.; Luo, Zhehui; Roman, Lee Anne (2009). "Fixed effects, random effects and GEE: What are the differences?". Statistics in Medicine 28: 221–239. doi:10.1002/sim.3478.
Further reading
 Christensen, Ronald (2002). Plane Answers to Complex Questions: The Theory of Linear Models (Third ed.). New York: Springer. ISBN 0387953612.
 Gujarati, Damodar N.; Porter, Dawn C. (2009). "Panel Data Regression Models". Basic Econometrics (Fifth international ed.). Boston: McGrawHill. pp. 591–616. ISBN 9780071276252.
 Wooldridge, Jeffrey M. (2013). "Random Effects Estimation". Introductory Econometrics: A Modern Approach (Fifth international ed.). Mason, OH: SouthWestern. pp. 474–478. ISBN 9781111534394.
External links
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