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The hinging hyperplanes: An alternative nonparametric representation of a production function

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  • Olesen, O.B.
  • Ruggiero, J.

Abstract

In this paper we propose hinging hyperplanes (HHs) as a flexible nonparametric representation of a concave or an S-shaped production function. We derive the HHs using expressions with focus on the distinction between hinge location and the bending along each hinge. We argue that the HHs approximation can be estimated using a fixed endogenous determined partitioning of the input space. Assuming a homothetic production function allows us to separate the S-shape scaling law and the underlying core function. We propose an estimation procedure where two HHs function approximations of the core function and the scaling law are estimated simultaneously. A closed form expression of the inverse of the piecewise linear inverse scaling law is proposed and proved.

Suggested Citation

  • Olesen, O.B. & Ruggiero, J., 2022. "The hinging hyperplanes: An alternative nonparametric representation of a production function," European Journal of Operational Research, Elsevier, vol. 296(1), pages 254-266.
  • Handle: RePEc:eee:ejores:v:296:y:2022:i:1:p:254-266
    DOI: 10.1016/j.ejor.2021.03.054
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    6. Antonio Peyrache, 2022. "A Homothetic Data Generated Technology," CEPA Working Papers Series WP042022, School of Economics, University of Queensland, Australia.

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