Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models

TL;DR

This paper develops a systematic method to derive moment restrictions in dynamic fixed effects logit models, enhancing the analysis of state dependence in binary and multinomial response data.

Contribution

It introduces a scalable, systematic procedure for constructing moment restrictions in complex dynamic logit models with fixed effects and exogenous regressors.

Findings

Derived moment restrictions for models with arbitrary lag order

Applied method to study drug consumption dynamics

Identified parameters and marginal effects in binary response models

Abstract

Dynamic logit models are popular tools in economics to measure state dependence. This paper introduces a new method to derive moment restrictions in a large class of such models with strictly exogenous regressors and fixed effects. We exploit the common structure of logit-type transition probabilities and elementary properties of rational fractions, to formulate a systematic procedure that scales naturally with model complexity (e.g the lag order or the number of observed time periods). We detail the construction of moment restrictions in binary response models of arbitrary lag order as well as first-order panel vector autoregressions and dynamic multinomial logit models. Identification of common parameters and average marginal effects is also discussed for the binary response case. Finally, we illustrate our results by studying the dynamics of drug consumption amongst young people inspired by Deza (2015).