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

Ayoola Femi Joshua

doi:doi:10.11648/j.ajtas.20170605.14

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

Ayoola Femi Joshua

Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 5)

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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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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TY  - JOUR
T1  - Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure
AU  - Ayoola Femi Joshua
Y1  - 2017/09/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170605.14
DO  - 10.11648/j.ajtas.20170605.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 248
EP  - 253
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170605.14
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.
VL  - 6
IS  - 5
ER  -

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Author Information

Ayoola Femi Joshua

Department of Statistics, University of Ibadan, Ibadan, Nigeria

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Plain Text BibTeX RIS

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}
}

Copy | Download

TY  - JOUR
T1  - Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure
AU  - Ayoola Femi Joshua
Y1  - 2017/09/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170605.14
DO  - 10.11648/j.ajtas.20170605.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 248
EP  - 253
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170605.14
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.
VL  - 6
IS  - 5
ER  -

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