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Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure

Received: 18 May 2017     Accepted: 12 June 2017     Published: 18 September 2017
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Abstract

This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T) with Absolute Bias (ABIAS) and Root Mean Squared Error (RMSE) were criterion for assessing the performances of the estimators. The estimators were then ordered according to their performances. Generally, the performance improved as the combinations of N and T increased in experiments. The ranking of the eight estimators for the experiment are in the order: PGLS (95%), SWAR (69%), NER (64%), WG (45%), AM (43%), WALHUS (37%), BG (36%) and POLS (28%). Panel generalised least squares estimator (PGLS) outperformed other estimators in the presence of OEHS, using POLS as a known benchmark to gauge the performance and the work will help in the choice of estimators when faced with empirical datasets that exhibit exponential heteroscedasticity.

Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 5)
DOI 10.11648/j.ajtas.20170605.14
Page(s) 248-253
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), 2017. Published by Science Publishing Group

Keywords

Panel Data, Estimators, Monte Carlo Simulation, One-Sided Exponential Heteroscedasticity, Performance

References
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[2] Baltagi, B. H. (2005), Econometric Analysis of Panel Data, 5thEdition Wiley, Chichester.
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[22] Green, H. W. (2008). Econometric Analysis (6th ed). Pearson, NJ: Prentice Hall.
[23] Baltagi, B. H., B. C. Jung and S. H. Song (2008), Testing for heteroscedasticity and serial correlation in a random effects panel data model, Working Paper number 111, Syracuse University, USA.
[24] Ayoola, F. J., Olubusoye, O. E. and Salisu, A. A. (2013). Panel Data Estimators in the Presence of Quadratic and Exponential forms of Heteroscedasticity. Journal of Science Research, Vol. 12: 263-274.
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Cite This Article
  • APA Style

    Ayoola Femi Joshua. (2017). Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure. American Journal of Theoretical and Applied Statistics, 6(5), 248-253. https://doi.org/10.11648/j.ajtas.20170605.14

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    ACS Style

    Ayoola Femi Joshua. Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure. Am. J. Theor. Appl. Stat. 2017, 6(5), 248-253. doi: 10.11648/j.ajtas.20170605.14

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    AMA Style

    Ayoola Femi Joshua. Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure. Am J Theor Appl Stat. 2017;6(5):248-253. doi: 10.11648/j.ajtas.20170605.14

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  • @article{10.11648/j.ajtas.20170605.14,
      author = {Ayoola Femi Joshua},
      title = {Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {5},
      pages = {248-253},
      doi = {10.11648/j.ajtas.20170605.14},
      url = {https://doi.org/10.11648/j.ajtas.20170605.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170605.14},
      abstract = {This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T) with Absolute Bias (ABIAS) and Root Mean Squared Error (RMSE) were criterion for assessing the performances of the estimators. The estimators were then ordered according to their performances. Generally, the performance improved as the combinations of N and T increased in experiments. The ranking of the eight estimators for the experiment are in the order: PGLS (95%), SWAR (69%), NER (64%), WG (45%), AM (43%), WALHUS (37%), BG (36%) and POLS (28%). Panel generalised least squares estimator (PGLS) outperformed other estimators in the presence of OEHS, using POLS as a known benchmark to gauge the performance and the work will help in the choice of estimators when faced with empirical datasets that exhibit exponential heteroscedasticity.},
     year = {2017}
    }
    

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    AB  - This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T) with Absolute Bias (ABIAS) and Root Mean Squared Error (RMSE) were criterion for assessing the performances of the estimators. The estimators were then ordered according to their performances. Generally, the performance improved as the combinations of N and T increased in experiments. The ranking of the eight estimators for the experiment are in the order: PGLS (95%), SWAR (69%), NER (64%), WG (45%), AM (43%), WALHUS (37%), BG (36%) and POLS (28%). Panel generalised least squares estimator (PGLS) outperformed other estimators in the presence of OEHS, using POLS as a known benchmark to gauge the performance and the work will help in the choice of estimators when faced with empirical datasets that exhibit exponential heteroscedasticity.
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Author Information
  • Department of Statistics, University of Ibadan, Ibadan, Nigeria

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