LASSO for Stochastic Frontier Models with Many Efficient Firms

We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L 1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency par...

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Bibliographic Details
Published in:Journal of business & economic statistics 2023-10, Vol.41 (4), p.1132-1142
Main Authors: Horrace, William C., Jung, Hyunseok, Lee, Yoonseok
Format: Article
Language:English
Subjects:
Adaptive LASSO
Fixed-effect stochastic frontier model
L1 regularization
Longitudinal studies
Panel data
Sign restriction
Zero inefficiency
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Summary:We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L 1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection criterion and an efficient optimization algorithm based on coordinate descent. We apply the method to estimate a group of efficient police officers who are best at detecting contraband in motor vehicle stops (i.e., search efficiency) in Syracuse, NY.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2022.2110881