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Decision tree-augmented stochastic frontier analysis

Author

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  • Deng, Ming-Yu
  • Kutlu, Levent
  • Mao, Xi

Abstract

We propose a decision tree-augmented stochastic frontier analysis (DT-SFA) strategy that embeds an SFA model-based recursive partitioning algorithm into the conventional classification and regression tree. Our methodology accounts for heterogeneity across subpopulations by generating segmented (local) SFA models, and makes it effectively visualized for analyzing and interpreting technical efficiency. Our empirical application to Chinese industrial enterprises illustrates that DT-SFA outperforms a standard single-frontier SFA in fit, which is consistent with substantial technological heterogeneity among firms. The approach preserves a parametric structure that supports statistical inference.

Suggested Citation

  • Deng, Ming-Yu & Kutlu, Levent & Mao, Xi, 2026. "Decision tree-augmented stochastic frontier analysis," Economics Letters, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:ecolet:v:264:y:2026:i:c:s0165176526001370
    DOI: 10.1016/j.econlet.2026.112943
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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