Bayesian Estimation of Technical Efficiency of a Single Input
AbstractThe authors propose estimation of a stochastic production frontier model within a Bayesian framework to obtain the posterior distribution of single-input-oriented technical efficiency at the firm level. This method can be used to estimate the environmental efficiency of agricultural production when the technology interaction with the environment is modeled via public inputs such as soil quality and environmental conditions. All computations are carried out using Markov chain Monte Carlo methods. The approach is illustrated through its application to production data from Ukrainian collective farms.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Food and Agricultural Policy Research Institute (FAPRI) at Iowa State University in its series Food and Agricultural Policy Research Institute (FAPRI) Publications with number 00-wp254.
Date of creation: Nov 2000
Date of revision:
Other versions of this item:
- Lyubov A. Kurkalova & Alicia L. Carriquiry, 2000. "Bayesian Estimation of Technical Efficiency of a Single Input," Center for Agricultural and Rural Development (CARD) Publications 00-wp254, Center for Agricultural and Rural Development (CARD) at Iowa State University.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
- Gstach, Dieter, 2005. "Estimating output targets to evaluate output-specific efficiencies: A statistical framework," European Journal of Operational Research, Elsevier, vol. 161(2), pages 564-578, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.