Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors

Authors

Mohamed Reda Abonazel, Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, EgyptFollow

Author Country (or Countries)

Egypt

Abstract

This paper considers stochastic parameter panel data models when the errors are first-order serially correlated. The feasible generalized least squares (FGLS) and simple mean group (SMG) estimators for these models have been reviewed and examined. The efficiency comparisons for these estimators have been carried when the regression parameters are stochastic, non-stochastic, and mixedstochastic. Monte Carlo simulation study and a real data application are given to evaluate the performance of FGLS and SMG estimators. The results indicate that, in small samples, SMG estimator is more reliable in most situations than FGLS estimators, especially when the model includes one or more non-stochastic parameter.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/jsap/070303

Recommended Citation

Reda Abonazel, Mohamed (2018) "Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors," Journal of Statistics Applications & Probability: Vol. 7: Iss. 3, Article 3.
DOI: http://dx.doi.org/10.18576/jsap/070303
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol7/iss3/3