Such models assist in controlling for unobserved heterogeneity, when this heterogeneity is constant over time: typically the ethnicity, the year and location. This constant can be removed from the data, for example by subtracting each individual's means from each of his observations before estimating the model.
A random effects model makes the assumption that the individual effects are randomly distributed. It is thus not the opposite of a fixed effects model. When the random effect is also assumed constant over time, we get fixed effects (i.e. a fixed effects model is a special case of random effects model). If the random effects assumption holds, the random effects model is more efficient than the fixed effects model. However, if this additional assumption does not hold (ie, if the Durbin-Wu test fails, the random effects model is not consistent.
Formally the model is
where is the dependent variable observed for individual i at time t, is the vector of coefficients, is a vector of regressors, is the individual effect and is the error term.
and the estimator is
where is the zero-mean regressor, and is the zero-mean dependent variable.
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