  This paper studies identification for a wide range of nonlinear panel data
models, including binary choice, ordered response, and other types of limited
dependent variable models. Our approach accommodates dynamic models with any
number of lagged dependent variables as well as other types of (potentially
contemporary) endogeneity. Our identification strategy relies on a partial
stationarity condition, which not only allows for an unknown distribution of
errors but also for temporal dependencies in errors. We derive partial
identification results under flexible model specifications and provide
additional support conditions for point identification. We demonstrate the
robust finite-sample performance of our approach using Monte Carlo simulations,
and apply the approach to analyze the empirical application of income
categories using various ordered choice models.
