Semiparametric Estimation of Stochastic Frontiers A Bayesian Penalized Approach
AbstractAlmost all previous approaches to estimating semiparametric frontier models, where the functional form for the production (cost) function is unknown, have been local nonparametric (ie. kernel) approaches. In this paper we use a penalized (ie. spline) approach. We show how this approach can be applied to a variety of frontier models, including panel models with fixed and random effects, within a Bayesian framework. We also apply our approach to different multivariate settings, including additive and additive with interaction models. The latter is a promising model because it is very flexible and does not suffer the severe curse of dimensionality problem common with fully nonparametric functions. We illustrate our method using a simulated example.
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Bibliographic InfoPaper provided by School of Economics, University of Queensland, Australia in its series CEPA Working Papers Series with number WP042003.
Date of creation: Aug 2003
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-06-02 (All new papers)
- NEP-ECM-2004-06-09 (Econometrics)
- NEP-EFF-2004-06-02 (Efficiency & Productivity)
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- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, Elsevier, vol. 6(1), pages 21-37, July.
- Park, B. U. & Simar, L., .
"Efficient semiparametric estimation in a stochastic frontier model,"
CORE Discussion Papers RP, UniversitÃ© catholique de Louvain, Center for Operations Research and Econometrics (CORE)
-1113, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Park, B.U. & Simar, L., 1992. "Efficient Semiparametric Estimation in Stochastic Frontier Model," Papers, Catholique de Louvain - Institut de statistique 9201, Catholique de Louvain - Institut de statistique.
- PARK, Byeong & SIMAR, Léopold, 1992. "Efficient semiparametric estimation in stochastic frontier model," CORE Discussion Papers, UniversitÃ© catholique de Louvain, Center for Operations Research and Econometrics (CORE) 1992013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Adams, Robert M & Berger, Allen N & Sickles, Robin C, 1999. "Semiparametric Approaches to Stochastic Panel Frontiers with Applications in the Banking Industry," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 17(3), pages 349-58, July.
- Smith, M. & Kohn, R., .
"Nonparametric Regression using Bayesian Variable Selection,"
Statistics Working Paper, Australian Graduate School of Management
_009, Australian Graduate School of Management.
- Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, Elsevier, vol. 75(2), pages 317-343, December.
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