Moment Conditions for Dynamic Panel Logit Models with Fixed Effects

This paper investigates the construction of moment conditions in discrete choice panel data with individual-specific fixed effects. We describe how to systematically explore the existence of moment conditions that do not depend on the fixed effects, and we demonstrate how to construct them when they exist. Our approach is closely related to the numerical “functional differencing” construction introduced in a seminal paper by Bonhomme, but our emphasis is to find explicit analytic expressions for the moment functions. We first explain the construction and give examples of such moment conditions in various models. Then, we focus on the dynamic binary choice logit model and explore the implications of the moment conditions for the identification and estimation of the model parameters that are common to all individuals.