R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models

Abonazel, Mohamed R. (2015): R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models. Forthcoming in: : pp. 1-6.

Abstract

These codes presented three functions for calculating three important estimators in dynamic panel data (DPD) models; these estimators are Arellano-Bond (1991), Arellano-Bover (1995), and Blundell-Bond (1998). All functions here need to the following variables: yit_1: dependent variable for DPD model; phi: the value of autoregressive coefficient; D.T_D.T: first-difference operator matrix of Arellano-Bond estimator; HD: instrumental variables of Arellano-Bond estimator; HL: instrumental variables of Arellano-Bover estimator; W: weighting matrix of Blundell-Bond estimator; HS: instrumental variables of Blundell-Bond estimator. Also, they need to the following R libraries: simex; plm; dlm. For more details about the theoretical bases and the developments of that estimators, see, e.g., Youssef et al. (2014a,b) and Youssef and Abonazel (2015). Moreover, these codes have been designed to enable the user to make a simulation study in this topic, such as the simulation study in Youssef et al. (2014b).

• Item Type: MPRA Paper
• Language: English
• Keywords: Dynamic panel data models; Generalized method of moments (GMM); Monte Carlo simulation; Two-step GMM estimations.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: 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 > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software

References

Abonazel, M. R. (2014). Some estimation methods for dynamic panel data models. Ph.D. thesis. Institute of Statistical Studies and Research. Cairo University.

Arellano, M., Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58:277–98.

Arellano, M., Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68:29-51.

Blundell, R., Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87:115–143.

Youssef, A. H., Abonazel, M. R. (2015). Alternative GMM estimators for first-order autoregressive panel model: an improving efficiency approach. Communications in Statistics-Simulation and Computation (in press). DOI: 10.1080/03610918.2015.1073307.

Youssef, A., El-sheikh, A., Abonazel, M. (2014a). Improving the efficiency of GMM estimators for dynamic panel models. Far East Journal of Theoretical Statistics 47:171–189.

Youssef, A., El-sheikh, A., Abonazel, M. (2014b). New GMM estimators for dynamic panel data models. International Journal of Innovative Research in Science, Engineering and Technology 3: 16414–16425.