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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