Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach

Mohamed Reda Abonazel

doi:doi:10.11648/j.ijssam.20180302.14

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Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach

Mohamed Reda Abonazel

Published in International Journal of Systems Science and Applied Mathematics (Volume 3, Issue 2)

Received: 5 June 2018 Accepted: 25 June 2018 Published: 25 July 2018

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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.

Published in	International Journal of Systems Science and Applied Mathematics (Volume 3, Issue 2)
DOI	10.11648/j.ijssam.20180302.14
Page(s)	37-51
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

First-Order Serial Correlation, Mixed-Stochastic Parameter Regression, Negative Variances, Pooled Least Squares, Simple Mean Group, Swamy’s Test

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APA Style

Mohamed Reda Abonazel. (2018). 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, 3(2), 37-51. https://doi.org/10.11648/j.ijssam.20180302.14

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ACS Style

Mohamed Reda Abonazel. Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach. Int. J. Syst. Sci. Appl. Math. 2018, 3(2), 37-51. doi: 10.11648/j.ijssam.20180302.14

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AMA Style

Mohamed Reda Abonazel. Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach. Int J Syst Sci Appl Math. 2018;3(2):37-51. doi: 10.11648/j.ijssam.20180302.14

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@article{10.11648/j.ijssam.20180302.14,
  author = {Mohamed Reda Abonazel},
  title = {Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {3},
  number = {2},
  pages = {37-51},
  doi = {10.11648/j.ijssam.20180302.14},
  url = {https://doi.org/10.11648/j.ijssam.20180302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20180302.14},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach
AU  - Mohamed Reda Abonazel
Y1  - 2018/07/25
PY  - 2018
N1  - https://doi.org/10.11648/j.ijssam.20180302.14
DO  - 10.11648/j.ijssam.20180302.14
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 37
EP  - 51
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20180302.14
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

Mohamed Reda Abonazel

Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt

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APA Style

Mohamed Reda Abonazel. (2018). 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, 3(2), 37-51. https://doi.org/10.11648/j.ijssam.20180302.14

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ACS Style

Mohamed Reda Abonazel. Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach. Int. J. Syst. Sci. Appl. Math. 2018, 3(2), 37-51. doi: 10.11648/j.ijssam.20180302.14

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AMA Style

Mohamed Reda Abonazel. Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach. Int J Syst Sci Appl Math. 2018;3(2):37-51. doi: 10.11648/j.ijssam.20180302.14

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@article{10.11648/j.ijssam.20180302.14,
  author = {Mohamed Reda Abonazel},
  title = {Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {3},
  number = {2},
  pages = {37-51},
  doi = {10.11648/j.ijssam.20180302.14},
  url = {https://doi.org/10.11648/j.ijssam.20180302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20180302.14},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach
AU  - Mohamed Reda Abonazel
Y1  - 2018/07/25
PY  - 2018
N1  - https://doi.org/10.11648/j.ijssam.20180302.14
DO  - 10.11648/j.ijssam.20180302.14
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 37
EP  - 51
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20180302.14
AB  - 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.
VL  - 3
IS  - 2
ER  -

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