A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data

Yangseon Kim and Peter Schmidt

Journal of Productivity Analysis, 2000, vol. 14, issue 2, 118 pages

Abstract: This paper appliesa large number of models to three previously-analyzed data sets,and compares the point estimates and confidence intervals fortechnical efficiency levels. Classical procedures include multiplecomparisons with the best, based on the fixed effects estimates;a univariate version, marginal comparisons with the best; bootstrappingof the fixed effects estimates; and maximum likelihood givena distributional assumption. Bayesian procedures include a Bayesianversion of the fixed effects model, and various Bayesian modelswith informative priors for efficiencies. We find that fixedeffects models generally perform poorly; there is a large payoffto distributional assumptions for efficiencies. We do not findmuch difference between Bayesian and classical procedures, inthe sense that the classical MLE based on a distributional assumptionfor efficiencies gives results that are rather similar to a Bayesiananalysis with the corresponding prior. Copyright Kluwer Academic Publishers 2000

Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1007801006988 (text/html)
Access to full text is restricted to subscribers.

Related works:
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:14:y:2000:i:2:p:91-118

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

DOI: 10.1023/A:1007801006988

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 ().