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This paper studies identification for a wide range of nonlinear panel datamodels, including binary choice, ordered repsonse, and other types of limiteddependent variable models. Our approach accommodates dynamic models with anynumber of lagged dependent variables as well as other types of (potentiallycontemporary) endogeneity. Our identification strategy relies on a partialstationarity condition, which not only allows for an unknown distribution oferrors but also for temporal dependencies in errors. We derive partialidentification results under flexible model specifications and provideadditional support conditions for point identification. We demonstrate therobust finite-sample performance of our approach using Monte Carlo simulations,and apply the approach to analyze the empirical application of incomecategories using various ordered choice models.

Identification of Dynamic Nonlinear Panel Models under Partial Stationarity