Model averaging estimators for the stochastic frontier model

Christopher Parmeter, Alan T. K. Wan and Xinyu Zhang
Additional contact information
Alan T. K. Wan: City University of Hong Kong
Xinyu Zhang: Chinese Academy of Sciences

Journal of Productivity Analysis, 2019, vol. 51, issue 2, No 1, 103 pages

Abstract: Abstract Model uncertainty is a prominent feature in many applied settings. This is certainty true in the efficiency analysis realm where concerns over the proper distributional specification of the error components of a stochastic frontier model is, generally, still open along with which variables influence inefficiency. Given the concern over the impact that model uncertainty is likely to have on the stochastic frontier model in practice, the present research proposes two distinct model averaging estimators, one which averages over nested classes of inefficiency distributions and another that has the ability to average over distinct distributions of inefficiency. Both of these estimators are shown to produce optimal weights when the aim is to uncover conditional inefficiency at the firm level. We study the finite-sample performance of the model average estimator via Monte Carlo experiments and compare with traditional model averaging estimators based on weights constructed from model selection criteria and present a short empirical application.

Keywords: Optimality; J-fold cross-validation; Efficiency; Model selection (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.com/10.1007/s11123-019-00547-8 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Model Averaging Estimators for the Stochastic Frontier Model (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:51:y:2019:i:2:d:10.1007_s11123-019-00547-8

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

DOI: 10.1007/s11123-019-00547-8

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 ().