 The hinging hyperplanes: An alternative nonparametric representation of a production functionO.B. Olesen and J. Ruggiero European Journal of Operational Research, 2022, vol. 296, issue 1, 254-266 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. Keywords: Data envelopment analysis; S-shaped nonparametric frontier estimation; Hinge functions; Canonical piecewise linear Representation; Fixed endogenous partitioning of the input space (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:296:y:2022:i:1:p:254-266 DOI: 10.1016/j.ejor.2021.03.054 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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