Quasi maximum likelihood estimation of dynamic panel data models

Robert Phillips

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 16, 3970-3986

Abstract: This article establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The article also provides an ECME algorithm for calculating levels QML estimates. Finally, it compares the finite-sample performance of levels and differenced QML estimators, the differenced generalized method of moments (GMM) estimator, and the system GMM estimator. The QML estimators usually have smaller— typically substantially smaller—bias and root mean squared errors than the panel data GMM estimators.

Date: 2018
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Journal Article: On quasi maximum-likelihood estimation of dynamic panel data models (2015)
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DOI: 10.1080/03610926.2017.1366521

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