Robust Estimation Procedure in Panel Data Model

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectiona...

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Main Author: Nurul Sima, Mohamad Shariff,
Format: Conference Paper
Language:en_US
Published: Amer Inst Physics 2015
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Online Access:http://ddms.usim.edu.my/handle/123456789/8979
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spelling my.usim-89792015-08-05T04:43:45Z Robust Estimation Procedure in Panel Data Model Nurul Sima, Mohamad Shariff, Cross Sectional Dependence Outliers Robust Method Panel Data The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered. 2015-08-05T04:43:45Z 2015-08-05T04:43:45Z 2014-01-01 Conference Paper 978-0-7354-1236-1 0094-243X http://ddms.usim.edu.my/handle/123456789/8979 en_US Amer Inst Physics
institution Universiti Sains Islam Malaysia
building USIM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universit Sains Islam i Malaysia
content_source USIM Institutional Repository
url_provider http://ddms.usim.edu.my/
language en_US
topic Cross Sectional Dependence
Outliers
Robust Method
Panel Data
spellingShingle Cross Sectional Dependence
Outliers
Robust Method
Panel Data
Nurul Sima, Mohamad Shariff,
Robust Estimation Procedure in Panel Data Model
description The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.
format Conference Paper
author Nurul Sima, Mohamad Shariff,
author_facet Nurul Sima, Mohamad Shariff,
author_sort Nurul Sima, Mohamad Shariff,
title Robust Estimation Procedure in Panel Data Model
title_short Robust Estimation Procedure in Panel Data Model
title_full Robust Estimation Procedure in Panel Data Model
title_fullStr Robust Estimation Procedure in Panel Data Model
title_full_unstemmed Robust Estimation Procedure in Panel Data Model
title_sort robust estimation procedure in panel data model
publisher Amer Inst Physics
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/8979
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score 13.222552