  We study the identification and estimation of structural parameters in
dynamic panel data logit models where decisions are forward-looking and the
joint distribution of unobserved heterogeneity and observable state variables
is nonparametric, i.e., fixed-effects model. We consider models with two
endogenous state variables: the lagged decision variable, and the time duration
in the last choice. This class of models includes as particular cases important
economic applications such as models of market entry-exit, occupational choice,
machine replacement, inventory and investment decisions, or dynamic demand of
differentiated products. The identification of structural parameters requires a
sufficient statistic that controls for unobserved heterogeneity not only in
current utility but also in the continuation value of the forward-looking
decision problem. We obtain the minimal sufficient statistic and prove
identification of some structural parameters using a conditional likelihood
approach. We apply this estimator to a machine replacement model.
