An Alternative System GMM Estimation in Dynamic Panel Models
AbstractThe system GMM estimator in dynamic panel data models which combines two moment conditions, i.e., for the differenced equation and for the model in levels, is known to be more efficient than the first-difference GMM estimator. However, an initial optimal weight matrix is not known for the system estimation procedure. Therefore, we suggest the use of 'a suboptimal weight matrix' which may reduce the finite sample bias whilst increasing its efficiency. Using the Kantorovich inequality, we find that the potential efficiency gain becomes large when the variance of individual effects increases compared to the variance of the idiosyncratic errors. (Our Monte Carlo experiments show that the small sample properties of the suboptimal system estimator are shown to be much more reliable than any other conventional system GMM estimator in terms of bias and efficiency.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Hi-Stat Discussion Paper Series with number d07-217.
Date of creation: Jul 2007
Date of revision:
Dynamic panel data; sub-optimal weighting matrix; KI upper boud;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-08-08 (All new papers)
- NEP-ECM-2007-08-08 (Econometrics)
- NEP-ETS-2007-08-08 (Econometric Time Series)
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- César Alonso-Borrego & Rocío Sánchez-Mangas, 2001. "Gmm Estimation Of A Production Function With Panel Data: An Application To Spanish Manufacturing Firms," Statistics and Econometrics Working Papers ws015527, Universidad Carlos III, Departamento de Estadística y Econometría.
- Kazuhiko Hayakawa, 2005.
"Small Sample Bias Propreties of the System GMM Estimator in Dynamic Panel Data Models,"
Hi-Stat Discussion Paper Series
d05-82, Institute of Economic Research, Hitotsubashi University.
- Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
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