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GLM models with fixed effects can be difficult to estimate, but a workaround is the conditional logistic recursive model. This model is helpful for logistic regression with fixed effects that cannot be consistently estimated. Such models are used in CEO selection studies and panel data sets with binary dependent variables. The conditional in the estimation addresses the issue of consistent differences between clusters, using a fixed effect estimated separately for each cluster. This technique is also known as the dummy variable model in panel data analysis.