TY - CHAP

T1 - Improving predictions of technical inefficiency

AU - Amsler, Christine

AU - James, Robert

AU - Прохоров, Артем Борисович

AU - Schmidt, Peter

PY - 2024/4/5

Y1 - 2024/4/5

N2 - 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.

AB - 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.

KW - Stochastic frontier analysis

KW - copulas

KW - inefficiency scores

KW - local random forest

KW - machine learning

KW - nonparametrics

KW - synthetic data

UR - https://www.mendeley.com/catalogue/3e3f3e93-2786-3e21-a011-e5e0abb179e2/

U2 - 10.1108/s0731-905320240000046011

DO - 10.1108/s0731-905320240000046011

M3 - Article in an anthology

SN - 978-1-83797-874-8

T3 - Advances in Econometrics

SP - 309

EP - 328

BT - Essays in Honor of Subal Kumbhakar

PB - Emerald Group Publishing Ltd.

ER -