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

Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models