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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01396528
Contributor : Thierry Denoeux <>
Submitted on : Monday, November 14, 2016 - 3:32:40 PM
Last modification on : Monday, December 14, 2020 - 9:52:39 AM
Long-term archiving on: : Tuesday, March 21, 2017 - 9:13:44 AM

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Songsak Sriboonchitta, Jianxu Liu, Wiboonpongse Aree, Thierry Denoeux. A double-copula stochastic frontier model with dependent error components and correction for sample selection. International Journal of Approximate Reasoning, Elsevier, 2017, 80, pp.174-184. ⟨10.1016/j.ijar.2016.08.006⟩. ⟨hal-01396528⟩

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