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GLM models with fixed effects are challenging to estimate consistently. Conditional logistic recursive model is a workaround for estimating fixed effects in logistic regression, often used in CEO selection studies and panel data sets with binary dependent variables. The conditional in the estimation refers to addressing unobservable heterogeneity. Fixed effects models use dummy variables to model differences between clusters.