Youssef, Ahmed H. and El-Sheikh, Ahmed A. and Abonazel, Mohamed R. (2014): New GMM Estimators for Dynamic Panel Data Models. Published in: International Journal of Innovative Research in Science, Engineering and Technology , Vol. 3, No. 10 (October 2014): pp. 16414-16425.
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Abstract
In dynamic panel data (DPD) models, the generalized method of moments (GMM) estimation gives efficient estimators. However, this efficiency is affected by the choice of the initial weighting matrix. In practice, the inverse of the moment matrix of the instruments has been used as an initial weighting matrix which led to a loss of efficiency. Therefore, we will present new GMM estimators based on optimal or suboptimal weighting matrices in GMM estimation. Monte Carlo study indicates that the potential efficiency gain by using these matrices. Moreover, the bias and efficiency of the new GMM estimators are more reliable than any other conventional GMM estimators.
Item Type: | MPRA Paper |
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Original Title: | New GMM Estimators for Dynamic Panel Data Models |
English Title: | New GMM Estimators for Dynamic Panel Data Models |
Language: | English |
Keywords: | Dynamic panel data, Generalized method of moments, Monte Carlo simulation, Optimal and suboptimal weighting matrices. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 68676 |
Depositing User: | Dr. Mohamed R. Abonazel |
Date Deposited: | 08 Jan 2016 14:23 |
Last Modified: | 26 Sep 2019 17:35 |
References: | Abonazel, M. R., “Some Estimation Methods for Dynamic Panel Data Models”, PhD Thesis, Institute of Statistical Studies and Research, Cairo University, 2014. Ahn, S. C., and Schmidt, P., “Efficient Estimation of Models for Dynamic Panel Data”, Journal of Econometrics, Vol. 68, pp. 5-28, 1995. Alvarez, J., and Arellano, M., “The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators”, Econometrica, Vol. 71, pp. 1121-1159, 2003. Arellano, M., and Bond, S., “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”, Review of Economic Studies, Vol. 58, pp. 277-98, 1991. Arellano, M., and Bover, O., “Another Look at the Instrumental Variable Estimation of Error-Components Models”, Journal of Econometrics, Vol. 68, pp. 29-51, 1995. Blundell, R., and Bond, S., “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”, Journal of Econometrics, Vol. 87, pp. 115-143, 1998. Bun, M., and Kiviet, J., “The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models”, Journal of Econometrics, Vol. 132, pp. 409-444, 2006. Hayakawa, K., “Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models”, Economics Letters, Vol. 95, pp. 32-38, 2007. Jung, H., and Kwon, H., “An Alternative System GMM Estimation in Dynamic Panel Models”, Hi-Stat Discussion Paper No. 217, Hitotsubashi University, 2007. Newey, W., and Smith, R., “Higher Order Properties of GMM and Generalized Empirical Likelihood Estimators”, Econometrica, Vol. 72, pp. 219-255, 2004. Windmeijer, F., “Efficiency Comparisons for a System GMM Estimator in Dynamic Panel Data Models”, In Innovations in Multivariate Statistical Analysis, Heijmans, R.D.H., Pollock, D.S.G. and Satorra, A., eds., A Festschrift for Heinz Neudecker, Advanced Studies in Theoretical and Applied Econometrics, Vol. 36, Kluwer Academic Publishers, Dordrecht (IFS working paper W98/1), 2000. Youssef, A. H., El-sheikh, A. A., and Abonazel, M. R., “Improving the Efficiency of GMM Estimators for Dynamic Panel Models”, Far East Journal of Theoretical Statistics, Vol. 47, pp. 171-189, 2014. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/68676 |