Description/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.
Document Type
Working Paper
Date
3-2022
Keywords
Panel Data, Fixed-Effect Stochastic Frontier Model, Adaptive LASSO, L1 Regularization, Sign Restriction, Zero Inefficiency
Language
English
Series
Working Papers Series
Acknowledgements
The authors are grateful to Badi Baltagi, Christopher Parmeter and the participants at the 15th European Workshop on Efficiency and Productivity Analysis, the 28th annual meeting of the Midwest Econometrics Group and the International Association for Applied Econometric 2019 Annual Conference for their valuable comments and suggestions.
Disciplines
Economic Policy | Economics | Public Affairs, Public Policy and Public Administration | Public Policy
ISSN
1525-3066
Recommended Citation
Horrace, William C.; Jung, Hyunseok; and Lee, Yoonseok, "LASSO for Stochastic Frontier Models with Many Efficient Firms" (2022). Center for Policy Research. 416.
https://surface.syr.edu/cpr/416
Source
Local input
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Economic Policy Commons, Economics Commons, Public Policy Commons
Additional Information
Working Paper No. 248