Smooth constrained frontier analysis

Christopher F. Parmeter, Jeffrey S. Racine

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Scopus citations

Abstract

Production frontiers (i.e., –production functions—) specify the maximum output of firms, industries, or economies as a function of their inputs. A variety of innovative methods have been proposed for estimating both –deterministic— and –stochastic— frontiers. However, existing approaches are either parametric in nature, rely on nonsmooth nonparametric methods, or rely on nonparametric or semiparametric methods that ignore theoretical axioms of production theory, each of which can be problematic. In this chapter we propose a class of smooth constrained nonparametric and semiparametric frontier estimators that may be particularly appealing to practitioners who require smooth (i.e., continuously differentiable) estimates that, in addition, are consistent with theoretical axioms of production.

Original languageEnglish (US)
Title of host publicationRecent Advances and Future Directions in Causality, Prediction, and Specification Analysis
Subtitle of host publicationEssays in Honor of Halbert L. White Jr
PublisherSpringer New York
Pages463-488
Number of pages26
ISBN (Electronic)9781461416531
ISBN (Print)9781461416524
DOIs
StatePublished - Jan 1 2013

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Keywords

  • Concavity ·
  • Constrained Kernel Estimator
  • Efficiency ·
  • Monotonicity ·

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics, Econometrics and Finance(all)

Cite this

Parmeter, C. F., & Racine, J. S. (2013). Smooth constrained frontier analysis. In Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr (pp. 463-488). Springer New York. https://doi.org/10.1007/978-1-4614-1653-1_18