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 language | English (US) |
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Title of host publication | Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis |
Subtitle of host publication | Essays in Honor of Halbert L. White Jr |
Publisher | Springer New York |
Pages | 463-488 |
Number of pages | 26 |
ISBN (Electronic) | 9781461416531 |
ISBN (Print) | 9781461416524 |
DOIs | |
State | Published - Jan 1 2013 |
Keywords
- Concavity ·
- Constrained Kernel Estimator
- Efficiency ·
- Monotonicity ·
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Economics, Econometrics and Finance(all)