Estimating Substantive Effects in Binary Outcome Panel Models: A Comparison

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Abstract

Dummy variable maximum likelihood estimation for binary response panel models struggles to estimate coefficients or substantive quantities in either short or rare event panels. The standard response is a conditional maximum likelihood that consistently estimates the coefficients on time-varying covariates but makes substantive effects impossible to compute. In light of this problem, multiple suggestions have appeared for computing these effects, but there is little to no guidance as to when one solution may be preferred to another. I address this question by comparing one of these approaches, a correlated random effects estimator, to the maximum likelihood dummy variable estimator. I find that when the number of within-group observations is small or events are rare, the correlated random effects estimator is preferred, but as panels get longer the difference between approaches fades.

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