We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking joint distribution unobserved heterogeneity observable state variables is nonparametric, i.e., fixed-effects model. consider with two endogenous variables: lagged decision variable, time duration last choice. This class includes as particular cases important...

A new kernel dimension reduction (KDR) method based on the gradient space of canonical functions is proposed for sufficient dimension reduction (SDR). Similar to existing KDR methods, this new method achieves SDR for arbitrary distributions, but with more flexibility and improved computational efficiency. The choice of loss function in cross-validation is discussed, and a two-stage screening pr...