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The Post Double LASSO for Efficiency Analysis

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  • Christopher Parmeter
  • Artem Prokhorov
  • Valentin Zelenyuk

Abstract

Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by deriving Neyman orthogonal moment conditions for this problem. Finally, an application is presented to illustrate key differences of the post-double LASSO compared to other approaches.

Suggested Citation

  • Christopher Parmeter & Artem Prokhorov & Valentin Zelenyuk, 2025. "The Post Double LASSO for Efficiency Analysis," Papers 2505.14282, arXiv.org.
    • Handle: RePEc:arx:papers:2505.14282
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    References listed on IDEAS

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    2. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
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