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) |
|---|---|
| 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 |
Fingerprint
Keywords
- Concavity ·
- Constrained Kernel Estimator
- Efficiency ·
- Monotonicity ·
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)
Cite this
Smooth constrained frontier analysis. / Parmeter, Christopher; Racine, Jeffrey S.
Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr. Springer New York, 2013. p. 463-488.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Smooth constrained frontier analysis
AU - Parmeter, Christopher
AU - Racine, Jeffrey S.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
KW - Concavity ·
KW - Constrained Kernel Estimator
KW - Efficiency ·
KW - Monotonicity ·
UR - http://www.scopus.com/inward/record.url?scp=84908502681&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908502681&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-1653-1_18
DO - 10.1007/978-1-4614-1653-1_18
M3 - Chapter
AN - SCOPUS:84908502681
SN - 9781461416524
SP - 463
EP - 488
BT - Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
PB - Springer New York
ER -