Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/59114
Title: An empirical likelihood estimator of stochastic frontier model
Authors: Pathairat Pastpipatkul
Woraphon Yamaka
Paravee Maneejuk
Songsak Sriboonchitta
Keywords: Physics and Astronomy
Issue Date: 26-Jul-2018
Abstract: © Published under licence by IOP Publishing Ltd. We consider a stochastic frontier model, with independent observation errors identically distributed with an unknown probability density function. Instead of maximizing the parametric version of the likelihood function, which requires knowledge about the error distribution, we replace the parametric likelihood with an empirical likelihood. A simulation and experiment study are presented to illustrate the finite-sample of this estimator in terms of its accuracy and robustness. Our proposed estimation is competitive and allows for better analysis of datasets than existing parametric methods.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051386757&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/59114
ISSN: 17426596
17426588
Appears in Collections:CMUL: Journal Articles

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