Bayesian stochastic frontier models under the skew-normal half-normal settings

Zheng Wei (), S. T. Boris Choy, Tonghui Wang and Xiaonan Zhu
Additional contact information
Zheng Wei: Texas A&M University - Corpus Christi
S. T. Boris Choy: The University of Sydney
Tonghui Wang: New Mexico State University
Xiaonan Zhu: University of North Alabama

Journal of Productivity Analysis, 2025, vol. 64, issue 1, No 5, 91 pages

Abstract: Abstract Recently, a skew-normal based stochastic frontier model has emerged as a promising tool for efficiency analysis. This paper introduces a Bayesian framework for statistical inference, integrating both informative and non-informative prior knowledge to estimate parameters of skew-normal distributions in stochastic frontier models. Through comprehensive evaluation using both simulation data and real data from a manufacturing productivity study, we demonstrate that the Bayesian approach provides more stable and accurate parameter estimates compared to the conventional maximum likelihood method. The results from both simulated and empirical analyses clearly highlight the superior performance of the Bayesian methodology, offering enhanced robustness and precision in estimating efficiency scores, thus contributing significantly to the advancement of stochastic frontier modeling.

Keywords: Bayesian statistics; Efficiency; Markov chain Monte Carlo; Skew-normal distribution; Stochastic frontier model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11123-025-00757-3 Abstract (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:64:y:2025:i:1:d:10.1007_s11123-025-00757-3

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

DOI: 10.1007/s11123-025-00757-3

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 ().