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Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach

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Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach. / Tsionas, Mike; Izzeldin, Marwan; Henningsen, Arne et al.
In: Empirical Economics, Vol. 62, No. 3, 31.03.2022, p. 1345-1363.

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Tsionas M, Izzeldin M, Henningsen A, Paravalos E. Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach. Empirical Economics. 2022 Mar 31;62(3):1345-1363. Epub 2021 May 2. doi: 10.1007/s00181-021-02060-0

Author

Tsionas, Mike ; Izzeldin, Marwan ; Henningsen, Arne et al. / Addressing endogeneity when estimating stochastic ray production frontiers : a Bayesian approach. In: Empirical Economics. 2022 ; Vol. 62, No. 3. pp. 1345-1363.

Bibtex

@article{85cecc7dbee948b59673fe32c4cd299c,
title = "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach",
abstract = "We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.",
keywords = "Stochastic ray production frontier, Technical inefficiency, Endogeneity, Bayesian inference, Model averaging",
author = "Mike Tsionas and Marwan Izzeldin and Arne Henningsen and Evaggelos Paravalos",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s00181-021-02060-0",
year = "2022",
month = mar,
day = "31",
doi = "10.1007/s00181-021-02060-0",
language = "English",
volume = "62",
pages = "1345--1363",
journal = "Empirical Economics",
issn = "0377-7332",
publisher = "Springer-Verlag",
number = "3",

}

RIS

TY - JOUR

T1 - Addressing endogeneity when estimating stochastic ray production frontiers

T2 - a Bayesian approach

AU - Tsionas, Mike

AU - Izzeldin, Marwan

AU - Henningsen, Arne

AU - Paravalos, Evaggelos

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s00181-021-02060-0

PY - 2022/3/31

Y1 - 2022/3/31

N2 - We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.

AB - We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.

KW - Stochastic ray production frontier

KW - Technical inefficiency

KW - Endogeneity

KW - Bayesian inference

KW - Model averaging

U2 - 10.1007/s00181-021-02060-0

DO - 10.1007/s00181-021-02060-0

M3 - Journal article

VL - 62

SP - 1345

EP - 1363

JO - Empirical Economics

JF - Empirical Economics

SN - 0377-7332

IS - 3

ER -