Document Type

Working Paper




Panel Data, Fixed-Effect Stochastic Frontier Model, Adaptive LASSO, L1 Regularization, Sign Restriction, Zero Inefficiency




Working Papers Series


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.


Economic Policy | Economics | Public Affairs, Public Policy and Public Administration | Public Policy


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.



Additional Information

Working Paper No. 248


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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