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Improving the Efficiency of GMM Estimators for Dynamic Panel Models

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  • Youssef, Ahmed H.
  • El-Sheikh, Ahmed A.
  • Abonazel, Mohamed R.

Abstract

In dynamic panel models, the generalized method of moments (GMM) has been used in many applications since it gives efficient estimators. This efficiency is affected by the choice of the initial weighted matrix. It is common practice to use the inverse of the moment matrix of the instruments as an initial weighted matrix. However, an initial optimal weighted matrix is not known, especially in the system GMM estimation procedure. Therefore, we present the optimal weighted matrix for level GMM estimator, and suboptimal weighted matrices for system GMM estimator, and use these matrices to increase the efficiency of GMM estimator. By using the Kantorovich inequality (KI), we find that the potential efficiency gain becomes large when the variance of individual effects increases compared with the variance of the errors.

Suggested Citation

  • Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "Improving the Efficiency of GMM Estimators for Dynamic Panel Models," MPRA Paper 68675, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:68675
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    References listed on IDEAS

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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
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    7. S. Liu & H. Neudecker, 1997. "Kantorovich inequalities and efficiency comparisons for several classes of estimators in linear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 51(3), pages 345-355, November.
    8. Housung Jung & Hyeog Ug Kwon, 2007. "An Alternative System GMM Estimation in Dynamic Panel Models," Hi-Stat Discussion Paper Series d07-217, Institute of Economic Research, Hitotsubashi University.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Citations

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    Cited by:

    1. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    2. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "New GMM Estimators for Dynamic Panel Data Models," MPRA Paper 68676, University Library of Munich, Germany.
    3. mohammed, habib, 2023. "Modeling Determinants of Private Banks Profitability in Ethiopia," MPRA Paper 116699, University Library of Munich, Germany.
    4. Charalampos Agiropoulos & Michael L. Polemis & Michael Siopsis & Sotiris Karkalakos, 2022. "Revisiting the finance‐growth nexus: A socioeconomic approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2762-2783, July.
    5. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    6. Abonazel, Mohamed R., 2015. "R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models," MPRA Paper 70627, University Library of Munich, Germany.
    7. mohammed, habib, 2023. "Modeling Determinants of Private Banks Profitability in Ethiopia," MPRA Paper 116817, University Library of Munich, Germany, revised 25 Mar 2023.
    8. Abonazel, Mohamed R., 2016. "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties," MPRA Paper 72586, University Library of Munich, Germany.

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    More about this item

    Keywords

    dynamic panel data; generalized method of moments; KI upper bound; optimal and suboptimal weighted matrices.;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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