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Technical and allocative inefficiency in production systems : a vine copula approach. / Zhai, Jian; James, Robert; Prokhorov, Artem.

в: Dependence Modeling, Том 10, № 1, 01.01.2022, стр. 145-158.

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Zhai, Jian ; James, Robert ; Prokhorov, Artem. / Technical and allocative inefficiency in production systems : a vine copula approach. в: Dependence Modeling. 2022 ; Том 10, № 1. стр. 145-158.

BibTeX

@article{63d97d008f8b460ba7985371864f9186,
title = "Technical and allocative inefficiency in production systems: a vine copula approach",
abstract = "Modeling the error terms in stochastic frontier models of production systems requires multivariate distributions with certain characteristics. We argue that canonical vine copulas offer a natural way to model the pairwise dependence between the two main error types that arise in production systems with multiple inputs. We introduce a vine copula construction that permits dependence between themagnitude (but not the sign) of the errors. By using a recently proposed family of copulas, we show how to construct a simulated likelihood based on C-vines. We discuss issues that arise in the estimation of such models and outline why such models better reflect the dependencies that arise in practice. Monte Carlo simulations and a classic empirical application to electricity generation plants illustrate the utility of the proposed approach. ",
keywords = "allocative inefficiency, production frontier, technical inefficiency, vine copulas",
author = "Jian Zhai and Robert James and Artem Prokhorov",
note = "Publisher Copyright: {\textcopyright} 2022 Jian Zhai et al.",
year = "2022",
month = jan,
day = "1",
doi = "10.1515/demo-2022-0108",
language = "English",
volume = "10",
pages = "145--158",
journal = "Dependence Modeling",
issn = "2300-2298",
publisher = "De Gruyter",
number = "1",

}

RIS

TY - JOUR

T1 - Technical and allocative inefficiency in production systems

T2 - a vine copula approach

AU - Zhai, Jian

AU - James, Robert

AU - Prokhorov, Artem

N1 - Publisher Copyright: © 2022 Jian Zhai et al.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Modeling the error terms in stochastic frontier models of production systems requires multivariate distributions with certain characteristics. We argue that canonical vine copulas offer a natural way to model the pairwise dependence between the two main error types that arise in production systems with multiple inputs. We introduce a vine copula construction that permits dependence between themagnitude (but not the sign) of the errors. By using a recently proposed family of copulas, we show how to construct a simulated likelihood based on C-vines. We discuss issues that arise in the estimation of such models and outline why such models better reflect the dependencies that arise in practice. Monte Carlo simulations and a classic empirical application to electricity generation plants illustrate the utility of the proposed approach.

AB - Modeling the error terms in stochastic frontier models of production systems requires multivariate distributions with certain characteristics. We argue that canonical vine copulas offer a natural way to model the pairwise dependence between the two main error types that arise in production systems with multiple inputs. We introduce a vine copula construction that permits dependence between themagnitude (but not the sign) of the errors. By using a recently proposed family of copulas, we show how to construct a simulated likelihood based on C-vines. We discuss issues that arise in the estimation of such models and outline why such models better reflect the dependencies that arise in practice. Monte Carlo simulations and a classic empirical application to electricity generation plants illustrate the utility of the proposed approach.

KW - allocative inefficiency

KW - production frontier

KW - technical inefficiency

KW - vine copulas

UR - http://www.scopus.com/inward/record.url?scp=85131739259&partnerID=8YFLogxK

U2 - 10.1515/demo-2022-0108

DO - 10.1515/demo-2022-0108

M3 - Article

AN - SCOPUS:85131739259

VL - 10

SP - 145

EP - 158

JO - Dependence Modeling

JF - Dependence Modeling

SN - 2300-2298

IS - 1

ER -

ID: 99587055