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Improving Predictions of Technical Inefficiency

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.
Authors
Christine Amsler Robert James Artem Prokhorov and Peter Schmidt
Keywords
Stochastic frontier analysis ; inefficiency scores ; copulas ; local random forest ; nonparametrics ; machine learning ; synthetic data ; C14 ; C23 ; C53 (search for similar items in EconPapers)
Rank
0.64
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Machine Learning
Series
A chapter in Essays in Honor of Subal Kumbhakar 2024 vol. 46 pp 309-328 from Emerald Group Publishing Limited
Time Added
2024/03/18 03:30
Total Downloads
0
Year Published
2024
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