Moment Conditions for Dynamic Panel Logit Models with Fixed Effects

TL;DR

This paper develops explicit analytic moment conditions for dynamic panel logit models with fixed effects, enhancing identification and estimation methods in discrete choice panel data analysis.

Contribution

It systematically constructs explicit moment conditions independent of fixed effects, extending the functional differencing approach with practical analytic expressions.

Findings

Explicit moment conditions derived for dynamic binary choice logit models

Improved identification of model parameters using these moment conditions

Enhanced estimation techniques for fixed effects models in panel data

Abstract

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 in Bonhomme (2012), 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 identification and estimation of the model parameters that are common to all individuals.