Fixed Effects

An indicator variable for each panel/subject is added and used to fit the model. Though often applied to the analysis of repeated measures, this approach has bias that increases with the number of subjects. If data include a very large number of subjects, the associated bias of the results can make this a very poor model choice.

Conditional Fixed Effects

Conditional fixed effects are commonly applied in logistic regression, Poisson regression, and negative binomial regression. A sufficient statistic for the subject effect is used to derive a conditional likelihood such that the subject-level effect is removed from the estimation.

While conditioning out the subject-level effect in this manner is algebraically attractive, interpretation of model results must continue to be in terms of the conditional likelihood. This may be difficult and the analyst must be willing to alter the original scientific questions of interest to questions in terms of the conditional likelihood.

Questions always arise as to whether some function of the independent variable might be more appropriate to use than the independent variable itself. For example, suppose X = Z2, where E(Y|Z) satisfies the logistic equation; then E(Y|X) does not.

Random Effects

The choice of a distribution for the random effect is driven too often by the need to find an analytic solution to the problem rather than by any scientific foundation. If we assume a normally distributed ...

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