LASSO for Stochastic Frontier Models with Many Efficient Firms

William Horrace, Hyunseok Jung and Yoonseok Lee

Journal of Business & Economic Statistics, 2023, vol. 41, issue 4, 1132-1142

Abstract: We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 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.

Date: 2023
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Working Paper: LASSO for Stochastic Frontier Models with Many Efficient Firms (2022)
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DOI: 10.1080/07350015.2022.2110881

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