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Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries

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  • Tsionas, Mike

Abstract

We propose smooth monotone concave probabilistic regression trees for the estimation of efficiency and productivity. In particular we modify these techniques to allow for the use of panel data which are often encountered in practice. Probabilistic regression trees provide smooth approximations and at the same time they exploit the versatility of standard regression trees in generating efficiently partitions of the space of the regressors to approximate the unknown frontier. We showcase the new techniques in a large sample of Chilean manufacturing firms.

Suggested Citation

  • Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:proeco:v:249:y:2022:i:c:s0925527322000858
    DOI: 10.1016/j.ijpe.2022.108492
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    2. Papaioannou, Grammatoula & Podinovski, Victor V., 2023. "Production technologies with ratio inputs and outputs," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1164-1178.
    3. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    4. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study," Operational Research, Springer, vol. 23(3), pages 1-33, September.

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