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  • paper_Oct_2018

    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

    Accepted author manuscript, 770 KB, PDF document

    Embargo ends: 22/10/20

    Available under license: CC BY-NC-ND

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A Bayesian semiparametric approach to stochastic frontiers and productivity

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>1/04/2019
<mark>Journal</mark>European Journal of Operational Research
Issue number1
Volume274
Number of pages12
Pages (from-to)391-402
Publication statusPublished
Early online date22/10/18
Original languageEnglish

Abstract

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.

Bibliographic note

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