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May 2012 GMM versus GQL inferences in semiparametric linear dynamic mixed models
R. Prabhakar Rao, Brajendra Sutradhar, V. N. Pandit
Braz. J. Probab. Stat. 26(2): 167-177 (May 2012). DOI: 10.1214/10-BJPS127

Abstract

Linear dynamic mixed models are commonly used for continuous panel data analysis in economic statistics. There exists generalized method of moments (GMM) and generalized quasi-likelihood (GQL) inferences for binary and count panel data models, the GQL estimation approach being more efficient than the GMM approach. The GMM and GQL estimating equations for the linear dynamic mixed model can not, however, be obtained from the respective estimating equations under the nonlinear models for binary and count data. In this paper, we develop the GMM and GQL estimation approaches for the linear dynamic mixed models and demonstrate that the GQL approach is more efficient than the GMM approach, also under such linear models. This makes the GQL approach uniformly more efficient than the GMM approach in estimating the parameters of both linear and nonlinear dynamic mixed models.

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R. Prabhakar Rao. Brajendra Sutradhar. V. N. Pandit. "GMM versus GQL inferences in semiparametric linear dynamic mixed models." Braz. J. Probab. Stat. 26 (2) 167 - 177, May 2012. https://doi.org/10.1214/10-BJPS127

Information

Published: May 2012
First available in Project Euclid: 23 January 2012

zbMATH: 1235.62137
MathSciNet: MR2880904
Digital Object Identifier: 10.1214/10-BJPS127

Keywords: consistency , dynamic dependence parameters , efficiency , random effects , regression effects , variance components

Rights: Copyright © 2012 Brazilian Statistical Association

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Vol.26 • No. 2 • May 2012
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