TY - JOUR

T1 - Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models

AU - Tran, Kien C.

AU - Tsionas, Mike G.

AU - Prokhorov, Artem B.

N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 304 (3), 2022 DOI: 10.1016/j.ejor.2022.04.039

PY - 2023/2/1

Y1 - 2023/2/1

N2 - This paper considers the estimation of a spatial autoregressive stochastic frontier model, where the production frontier coefficients, as well as the spatial parameter, are allowed to depend on a set of observed environmental factors. The inefficiency term is multiplicatively separable into a scaling function of either the same or totally different set of environmental factors and a standard half-normal random variable. A two-step semiparametric procedure is developed using a combination of local GMM and maximum likelihood approaches. We also derive the estimators of direct and indirect average partial effects and predictors of technical efficiency. We work out the asymptotic theory for the proposed second step estimator and propose a test of the relevance of the environmental factors. A special case of the model where the spatial parameter is a non-zero constant is also considered. The finite sample behaviour of the proposed estimator and test are examined using Monte Carlo simulations. An empirical application to the Chinese chemical industry is included to illustrate the usefulness of our proposed models and methods.

AB - This paper considers the estimation of a spatial autoregressive stochastic frontier model, where the production frontier coefficients, as well as the spatial parameter, are allowed to depend on a set of observed environmental factors. The inefficiency term is multiplicatively separable into a scaling function of either the same or totally different set of environmental factors and a standard half-normal random variable. A two-step semiparametric procedure is developed using a combination of local GMM and maximum likelihood approaches. We also derive the estimators of direct and indirect average partial effects and predictors of technical efficiency. We work out the asymptotic theory for the proposed second step estimator and propose a test of the relevance of the environmental factors. A special case of the model where the spatial parameter is a non-zero constant is also considered. The finite sample behaviour of the proposed estimator and test are examined using Monte Carlo simulations. An empirical application to the Chinese chemical industry is included to illustrate the usefulness of our proposed models and methods.

KW - Productivity and Competitiveness

KW - Efficiency Spillover

KW - Local GMM and Maximum likelihood

KW - Smooth Coefficient

KW - Spatial Autoregressive Stochastic Frontier

U2 - 10.1016/j.ejor.2022.04.039

DO - 10.1016/j.ejor.2022.04.039

M3 - Journal article

VL - 304

SP - 1189

EP - 1199

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -