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Maximum Likelihood and GMM Estimation of Dynamic Panel Data Models with Fixed Effects

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Author Info
Hugo Kruiniger () (Queen Mary, University of London)

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Abstract

This paper considers inference procedures for two types of dynamic linear panel data models with fixed effects (FE). First, it shows that the closures of stationary ARMAFE models can be consistently estimated by Conditional Maximum Likelihood Estimators and it derives their asymptotic distributions. Then it presents an asymptotically equivalent Minimum Distance Estimator which permits an analytic comparison between the CMLE for the ARFE(1) model and the GMM estimators that have been considered in the literature. The CMLE is shown to be asymptotically less efficient than the most efficient GMM estimator when N tends to infinity but T is fixed. Under normality some of the moment conditions become asymptotically redundant and the CMLE attains the Cramer-Rao lowerbound when T tends to infinity as well. The paper also presents likelihood based unit root tests. Finally, the properties of CML, GMM, and Modified ML estimators for dynamic panel data models that condition on the initial observations are studied and compared. It is shown that for finite T the MMLE is less efficient than the most efficient GMM estimator.

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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 429.

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Date of creation: Dec 2000
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Handle: RePEc:qmw:qmwecw:wp429

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Related research
Keywords: Dynamic panel data models Fixed effects GMM Conditional ML Modified ML Bayesian methods (Asymptotic) redundancy Cramer-Rao and semiparametric efficiency bounds Unit root tests Parameter on boundary problem

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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