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Identification of Dynamic Nonlinear Panel Models under Partial Stationarity

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  • Wayne Yuan Gao
  • Rui Wang

Abstract

This paper studies identification for a wide range of nonlinear panel data models, including binary choice, ordered repsonse, 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.

Suggested Citation

  • Wayne Yuan Gao & Rui Wang, 2023. "Identification of Dynamic Nonlinear Panel Models under Partial Stationarity," Papers 2401.00264, arXiv.org, revised Jan 2024.
    • Handle: RePEc:arx:papers:2401.00264
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    References listed on IDEAS

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    1. Andrew Chesher & Adam Rosen & Yuanqi Zhang, 2023. "Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions," CeMMAP working papers 20/23, Institute for Fiscal Studies.
    2. Manski, Charles F, 1987. "Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data," Econometrica, Econometric Society, vol. 55(2), pages 357-362, March.
    3. Wayne Yuan Gao & Ming Li, 2020. "Robust Semiparametric Estimation in Panel Multinomial Choice Models," Papers 2009.00085, arXiv.org.
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