LASSO for Stochastic Frontier Models with Many Efficient Firms

William C. Horrace (), Hyunseok Jung () and Yoonseok Lee
Additional contact information
William C. Horrace: Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244, https://www.maxwell.syr.edu/directory/william-c-horrace
Hyunseok Jung: Department of Economics, University of Arkansas, Fayetteville, AR 72701, https://walton.uark.edu/directory/all-faculty/uid/hj020/name/Hyunseok+Jung/

No 248, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: We apply the adaptive LASSO (Zou, 2006) to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 penalty with sign restrictions for firm-level inefficiencies allows simultaneous estimation of the maximal efficiency and firm-level inefficiency parameters, which results in a faster rate of convergence of the corresponding estimators than the least-squares dummy variable approach. We show that the estimator possesses the oracle property and selection consistency still holds with our proposed tuning parameter selection criterion. We also propose 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.

Keywords: Panel Data; Fixed-Effect Stochastic Frontier Model; Adaptive LASSO; L1 Regularization; Sign Restriction; Zero Inefficiency (search for similar items in EconPapers)
JEL-codes: C14 C23 D24 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2022-03
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
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Citations: View citations in EconPapers (1)

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https://surface.syr.edu/cpr/416/ (application/pdf)

Related works:
Journal Article: LASSO for Stochastic Frontier Models with Many Efficient Firms (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:248

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