Estimation and testing of stochastic frontier models using variational Bayes

Gholamreza Hajargasht () and William Griffiths ()

Journal of Productivity Analysis, 2018, vol. 50, issue 1, No 1, 24 pages

Abstract: Abstract We show how a wide range of stochastic frontier models can be estimated relatively easily using variational Bayes. We derive approximate posterior distributions and point estimates for parameters and inefficiency effects for (a) time invariant models with several alternative inefficiency distributions, (b) models with time varying effects, (c) models incorporating environmental effects, and (d) models with more flexible forms for the regression function and error terms. Despite the abundance of stochastic frontier models, there have been few attempts to test the various models against each other, probably due to the difficulty of performing such tests. One advantage of the variational Bayes approximation is that it facilitates the computation of marginal likelihoods that can be used to compare models. We apply this idea to test stochastic frontier models with different inefficiency distributions. Estimation and testing is illustrated using three examples.

Keywords: Technical efficiency; Marginal likelihood; Time-varying panel; Environmental effects; Mixture; Semiparametric model (search for similar items in EconPapers)
JEL-codes: C11 C12 C23 D24 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11123-018-0531-0 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Estimation and Testing of Stochastic Frontier Models using Variational Bayes (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:50:y:2018:i:1:d:10.1007_s11123-018-0531-0

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2

DOI: 10.1007/s11123-018-0531-0

Access Statistics for this article

Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski

More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().