Volume 3, Issue 2, March 2018, Page: 37-51
Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach
Mohamed Reda Abonazel, Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt
Received: Jun. 5, 2018;       Accepted: Jun. 25, 2018;       Published: Jul. 25, 2018
DOI: 10.11648/j.ijssam.20180302.14      View  497      Downloads  22
Abstract
This paper considers panel data models when the errors are first-order serially correlated as well as with stochastic regression parameters. The generalized least squares (GLS) estimators for these models have been derived and examined in this paper. Moreover, an alternative estimator for GLS estimators in small samples has been proposed, this estimator is called simple mean group (SMG). The efficiency comparisons for GLS and SMG estimators have been carried out. The Monte Carlo studies indicate that SMG estimator is more reliable in most situations than the GLS estimators, especially when the model includes one or more non-stochastic parameter.
Keywords
First-Order Serial Correlation, Mixed-Stochastic Parameter Regression, Negative Variances, Pooled Least Squares, Simple Mean Group, Swamy’s Test
To cite this article
Mohamed Reda Abonazel, Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach, International Journal of Systems Science and Applied Mathematics. Vol. 3, No. 2, 2018, pp. 37-51. doi: 10.11648/j.ijssam.20180302.14
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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