A double-copula stochastic frontier model with dependent error components and correction for sample selection

© 2016 Elsevier Inc. In the standard stochastic frontier model with sample selection, the two components of the error term are assumed to be independent, and the joint distribution of the unobservable in the selection equation and the symmetric error term in the stochastic frontier equation is assum...

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Main Authors: Sriboonchitta S., Liu J., Wiboonpongse A., Denoeux T.
Format: Journal
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84987934284&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/41119
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-411192017-09-28T04:15:42Z A double-copula stochastic frontier model with dependent error components and correction for sample selection Sriboonchitta S. Liu J. Wiboonpongse A. Denoeux T. © 2016 Elsevier Inc. In the standard stochastic frontier model with sample selection, the two components of the error term are assumed to be independent, and the joint distribution of the unobservable in the selection equation and the symmetric error term in the stochastic frontier equation is assumed to be bivariate normal. In this paper, we relax these assumptions by using two copula functions to model the dependences between the symmetric and inefficiency terms on the one hand, and between the errors in the sample selection and stochastic frontier equation on the other hand. Several families of copula functions are investigated, and the best model is selected using the Akaike Information Criterion (AIC). The methodology was applied to a sample of 200 rice farmers from Northern Thailand. The main findings are that (1) the double-copula stochastic frontier model outperforms the standard model in terms of AIC, and (2) the standard model underestimates the technical efficiency scores, potentially resulting in wrong conclusions and recommendations. 2017-09-28T04:15:42Z 2017-09-28T04:15:42Z 2017-01-01 Journal 0888613X 2-s2.0-84987934284 10.1016/j.ijar.2016.08.006 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84987934284&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41119
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 2016 Elsevier Inc. In the standard stochastic frontier model with sample selection, the two components of the error term are assumed to be independent, and the joint distribution of the unobservable in the selection equation and the symmetric error term in the stochastic frontier equation is assumed to be bivariate normal. In this paper, we relax these assumptions by using two copula functions to model the dependences between the symmetric and inefficiency terms on the one hand, and between the errors in the sample selection and stochastic frontier equation on the other hand. Several families of copula functions are investigated, and the best model is selected using the Akaike Information Criterion (AIC). The methodology was applied to a sample of 200 rice farmers from Northern Thailand. The main findings are that (1) the double-copula stochastic frontier model outperforms the standard model in terms of AIC, and (2) the standard model underestimates the technical efficiency scores, potentially resulting in wrong conclusions and recommendations.
format Journal
author Sriboonchitta S.
Liu J.
Wiboonpongse A.
Denoeux T.
spellingShingle Sriboonchitta S.
Liu J.
Wiboonpongse A.
Denoeux T.
A double-copula stochastic frontier model with dependent error components and correction for sample selection
author_facet Sriboonchitta S.
Liu J.
Wiboonpongse A.
Denoeux T.
author_sort Sriboonchitta S.
title A double-copula stochastic frontier model with dependent error components and correction for sample selection
title_short A double-copula stochastic frontier model with dependent error components and correction for sample selection
title_full A double-copula stochastic frontier model with dependent error components and correction for sample selection
title_fullStr A double-copula stochastic frontier model with dependent error components and correction for sample selection
title_full_unstemmed A double-copula stochastic frontier model with dependent error components and correction for sample selection
title_sort double-copula stochastic frontier model with dependent error components and correction for sample selection
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84987934284&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/41119
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