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Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors

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  • Enrique Moral-Benito

Abstract

This article discusses the likelihood-based estimation of panel data models with individual-specific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as subsystem limited information maximum likelihood (ssLIML), is asymptotically equivalent to standard panel generalized method of moment as N →∞ for fixed T but tends to present smaller biases in finite samples as illustrated in simulation experiments. Simulation results also indicate that the estimator is preferred to other alternatives available in the literature in terms of finite-sample performance. Finally, to provide an empirical illustration, I revisit the evidence on the relationship between income and democracy in a panel of countries using the proposed estimator.

Suggested Citation

  • Enrique Moral-Benito, 2013. "Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 451-472, October.
    • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:4:p:451-472
    DOI: 10.1080/07350015.2013.818003
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    References listed on IDEAS

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    1. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    2. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
    3. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    4. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
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