On the estimation of total factor productivity: A novel Bayesian non-parametric approach
Mike G. Tsionas and
Michael Polemis ()
European Journal of Operational Research, 2019, vol. 277, issue 3, 886-902
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.
Keywords: Manufacturing; Estimation of TFP; Non parametric models; Bayesian analysis; Markov Chain Monte Carlo (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:277:y:2019:i:3:p:886-902
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().