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Robust Nonparametric Partial Frontier Input Distance Function Estimates: Testing and Rejecting Convexity

Author

Listed:
  • Shirong ZHAO

    (School of Finance, Dongbei University of Finance and Economics, Dalian, Liaoning, 116025, China)

  • Kristiaan KERSTENS

    (IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France)

  • Tiantian REN

    (IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France)

Abstract

Statistical tests of the convexity assumption for full frontier technologies are still rather rarely applied. We employ existing order-a and order-m partial frontier estimators for production technologies. Since these partial frontier technology estimators have favourable statistical properties, this permits testing convexity using a simple statistical test comparing densities. Using a panel of electric power generation plants in the US we find strong evidence against convexity for most years using both order-a and order-m partial production frontiers. These new empirical results imply that empirical researchers must be cautious about invoking the –mostly implicit– convexity assumption when estimating technologies. Anyway, it is clear that convexity needs to be tested.

Suggested Citation

  • Shirong ZHAO & Kristiaan KERSTENS & Tiantian REN, 2026. "Robust Nonparametric Partial Frontier Input Distance Function Estimates: Testing and Rejecting Convexity," Working Papers 2026-EQM-02, IESEG School of Management.
  • Handle: RePEc:ies:wpaper:e202604
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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