Stochastic Frontiers and Asymmetric Information Models
This article is an attempt to shed light on the specification and identification of inefficiency in stochastic frontiers. We consider the case of a regulated firm or industry. Applying a simple principal-agent framework that accounts for informational asymmetries to this context, we derive the associated production and cost frontiers. Noticeably this approach yields a decomposition of inefficiency into two components: The first component is a pure random term while the second component depends on the unobservable actions taken by the agent (the firm). This result provides a theoretical foundation to the usual specification applied in the literature on stochastic frontiers. An application to a panel data set of French urban transport networks serves as an illustration.
|Date of creation:||2002|
|Date of revision:|
|Publication status:||Published, Journal of Productivity Analysis, 2002, 18, 145-159|
|Note:||View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00622849/en/|
|Contact details of provider:|| Web page: http://hal.archives-ouvertes.fr/ |
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- Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
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- Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1989.
"Production Frontiers With Cross-Sectinal And Time-Series Variation In Efficiency Levels,"
89-18, C.V. Starr Center for Applied Economics, New York University.
- Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
- Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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