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Identification without assuming mean stationarity: quasi–maximum likelihood estimation of dynamic panel models with endogenous regressors

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  • Hugo Kruiniger

Abstract

SummaryLinear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi–maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi–) maximum likelihood estimators in terms of precision, robustness, and/or ease of computation.

Suggested Citation

  • Hugo Kruiniger, 2021. "Identification without assuming mean stationarity: quasi–maximum likelihood estimation of dynamic panel models with endogenous regressors," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 417-441.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:3:p:417-441.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa036
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    Cited by:

    1. Yan Sun & Wei Huang, 2022. "Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 93-114, January.

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