TY - JOUR

T1 - On the estimation of total factor productivity

T2 - A novel Bayesian non-parametric approach

AU - Tsionas, M.G.

AU - Polemis, M.L.

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, 277, 3, 2019 DOI: 10.1016/j.ejor.2019.03.035

PY - 2019/9/19

Y1 - 2019/9/19

N2 - This paper provides an alternative general empirical method for the estimation of Total Factor Productivity (TFP). We use a decomposition which allows non-parametric estimation and at the same time addresses the issue of endogeneity of inputs. In this way, we also deal with the unavailability of input prices which is common in the TFP literature. We apply the new techniques to U.S four-digit manufacturing data using a novel Bayesian nonparametric model based on local likelihood. We use Markov Chain Monte Carlo (MCMC) techniques organized around the method of Girolami and Calderhead (2011). We compare and contrast the estimates from the proposed new method with standard parametric methods such as the translog, the Generalized Leontief and the Normalized Quadratic and we also propose novel diagnostic tests for correct specification and validity of instruments. We show that parametric methods lead to biased estimation of TFP growth. Our empirical findings reveal that the new model passes successfully a battery of robustness checks including diagnostic testing and tests for weak identification as well as weak instruments. Finally policy implications relating to the nature of TFP growth are also provided.

AB - This paper provides an alternative general empirical method for the estimation of Total Factor Productivity (TFP). We use a decomposition which allows non-parametric estimation and at the same time addresses the issue of endogeneity of inputs. In this way, we also deal with the unavailability of input prices which is common in the TFP literature. We apply the new techniques to U.S four-digit manufacturing data using a novel Bayesian nonparametric model based on local likelihood. We use Markov Chain Monte Carlo (MCMC) techniques organized around the method of Girolami and Calderhead (2011). We compare and contrast the estimates from the proposed new method with standard parametric methods such as the translog, the Generalized Leontief and the Normalized Quadratic and we also propose novel diagnostic tests for correct specification and validity of instruments. We show that parametric methods lead to biased estimation of TFP growth. Our empirical findings reveal that the new model passes successfully a battery of robustness checks including diagnostic testing and tests for weak identification as well as weak instruments. Finally policy implications relating to the nature of TFP growth are also provided.

KW - Bayesian analysis

KW - Estimation of TFP

KW - Manufacturing

KW - Markov Chain Monte Carlo

KW - Non parametric models

KW - Instrument testing

KW - Manufacture

KW - Markov processes

KW - Monte Carlo methods

KW - Productivity

KW - Public policy

KW - Bayesian Analysis

KW - Bayesian nonparametric modeling

KW - Generalized Leontief

KW - Markov Chain Monte-Carlo

KW - Non-parametric estimations

KW - Non-parametric model

KW - Nonparametric approaches

KW - Total factor productivity

KW - Parameter estimation

U2 - 10.1016/j.ejor.2019.03.035

DO - 10.1016/j.ejor.2019.03.035

M3 - Journal article

VL - 277

SP - 886

EP - 902

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -