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Improving Predictions of Technical Inefficiency

Christine Amsler (Department of Economics, Michigan State University, East Lansing, MI, USA)

Robert James (University of Sydney Business School, Darlington, NSW, Australia)

Artem Prokhorov (University of Sydney Business School, Darlington, NSW, Australia; Center for Econometrics and Business Analytics, St.Petersburg State University, St.Petersburg, Russia; and Centre interuniversitaire de recherche en économie quantitative, Montreal, Canada)

Peter Schmidt (Department of Economics, Michigan State University, East Lansing, MI, USA)

Essays in Honor of Subal Kumbhakar

ISBN: 978-1-83797-874-8, eISBN: 978-1-83797-873-1

Publication date: 5 April 2024

Abstract

The traditional predictor of technical inefficiency proposed by Jondrow, 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.

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Acknowledgements

Acknowledgments

Helpful comments from participants of EcoSta 2022, EWEPA 2022 and iCEBA 2022, as well as seminar participants at Florida State University, Macquarie University and Higher School of Economics are gratefully acknowledged. The use of the University of Sydney’s high-performance computing cluster, Artemis, is acknowledged. James’s research for this chapter was supported by a grant from the Australian Research Council (Project DP200103549). Prokhorov’s research for this chapter was supported by a grant from the Russian Science Foundation (Project No. 20-18-00365).

Citation

Amsler, C., James, R., Prokhorov, A. and Schmidt, P. (2024), "Improving Predictions of Technical Inefficiency", Parmeter, C.F., Tsionas, M.G. and Wang, H.-J. (Ed.) Essays in Honor of Subal Kumbhakar (Advances in Econometrics, Vol. 46), Emerald Publishing Limited, Leeds, pp. 309-328. https://doi.org/10.1108/S0731-905320240000046011

Publisher

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Emerald Publishing Limited