Rights statement: 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, 274, 1, 2019, DOI: 10.1016j.ejor.2018.10.026
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A Bayesian semiparametric approach to stochastic frontiers and productivity
AU - Tsionas, M.G.
AU - Mallick, S.K.
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, 274, 1, 2019, DOI: 10.1016j.ejor.2018.10.026
PY - 2019/4/1
Y1 - 2019/4/1
N2 - In this paper we take up the analysis of production functions / frontiers removing the assumptions of known functional form for the productivity equation, given the heterogeneity of productivity and the endogeneity of inputs at firm level. The assumption of exogenous regressors is removed through taking account of the first order conditions of profit maximization. We introduce latent dynamic stochastic productivity in our framework and perform Bayesian analysis using a Sequential Monte Carlo Particle-Filtering approach. We investigate the performance of the new approach relative to alternative methods in the literature, in a substantive application to Indian non-financial firms, and find that total factor productivity (TFP) growth has remained stagnant at firm level in India despite rapid growth at the aggregate level, with technical efficiency or catching-up effect driving TFP growth in the recent years rather than technological progress or frontier shift. © 2018 Elsevier B.V.
AB - In this paper we take up the analysis of production functions / frontiers removing the assumptions of known functional form for the productivity equation, given the heterogeneity of productivity and the endogeneity of inputs at firm level. The assumption of exogenous regressors is removed through taking account of the first order conditions of profit maximization. We introduce latent dynamic stochastic productivity in our framework and perform Bayesian analysis using a Sequential Monte Carlo Particle-Filtering approach. We investigate the performance of the new approach relative to alternative methods in the literature, in a substantive application to Indian non-financial firms, and find that total factor productivity (TFP) growth has remained stagnant at firm level in India despite rapid growth at the aggregate level, with technical efficiency or catching-up effect driving TFP growth in the recent years rather than technological progress or frontier shift. © 2018 Elsevier B.V.
KW - Endogenous Regressors
KW - Particle-filtering
KW - Productivity and competitiveness
KW - Sequential Monte Carlo
KW - Stochastic frontier model
KW - Monte Carlo methods
KW - Productivity
KW - Stochastic systems
KW - Telecommunication industry
KW - Particle Filtering
KW - Stochastic frontier models
KW - Stochastic models
U2 - 10.1016/j.ejor.2018.10.026
DO - 10.1016/j.ejor.2018.10.026
M3 - Journal article
VL - 274
SP - 391
EP - 402
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
ER -