Improving Predictions of Technical Inefficiency

Christine Amsler, Robert James, Artem Prokhorov and Peter Schmidt

A chapter in Essays in Honor of Subal Kumbhakar, 2024, vol. 46, pp 309-328 from Emerald Group Publishing Limited

Abstract: The traditional predictor of technical inefficiency proposed byJondrow, Lovell, Materov, and Schmidt (1982)is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Keywords: Stochastic frontier analysis; inefficiency scores; copulas; local random forest; nonparametrics; machine learning; synthetic data; C14; C23; C53 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 1-905320240000046011
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
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:eme:aecozz:s0731-905320240000046011

DOI: 10.1108/S0731-905320240000046011

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().