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Stochastic frontier models

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  • Wang, Hung-Jen

Abstract

The stochastic frontier model was first proposed in the context of production function estimation to account for the effect of technical inefficiency. The inefficiency causes actual output to fall below the potential level (that is, the production frontier) and also raises production cost above the minimum level (that is, the cost frontier). Recent applications of the model are found in many fields of study including labour, finance, and economic growth. In these applications, the observed outcome (of wages, investment, and so on) is modelled as being deviating from a frontier level in one direction owing to factors such as information asymmetry.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 31079.

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Date of creation: 18 Mar 2006
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Handle: RePEc:pra:mprapa:31079

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Related research

Keywords: aftermarkets; allocative inefficiency; convergence; copulas; cost functions; duality; financing constraints; fixed-effect panel estimators; labour market search models; likelihood functions; nonparametric estimation; production function estimation; production functions; semiparametric estimation; stochastic cost frontiers; stochastic frontier models; technical inefficiency; technological catch-up; truncated distributions;

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References

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  1. Schmidt, Peter & Lin, Tsai-Fen, 1984. "Simple tests of alternative specifications in stochastic frontier models," Journal of Econometrics, Elsevier, Elsevier, vol. 24(3), pages 349-361, March.
  2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, Elsevier, vol. 19(2-3), pages 233-238, August.
  3. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 38(3), pages 387-399, July.
  4. Greene, William H., 1980. "On the estimation of a flexible frontier production model," Journal of Econometrics, Elsevier, Elsevier, vol. 13(1), pages 101-115, May.
  5. 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.
  6. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "The Joint Measurement of Technical and Allocative Inefficiencies: An Application of Bayesian Inference in Nonlinear Random-Effects Models," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 736-747, September.
  7. Hunt-McCool, Janet & Koh, Samuel C & Francis, Bill B, 1996. "Testing for Deliberate Underpricing in the IPO Premarket: A Stochastic Frontier Approach," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 9(4), pages 1251-69.
  8. Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, Elsevier, vol. 88(3), pages 300-305, September.
  9. Meeusen, Wim & van den Broeck, J, 1977. "Technical Efficiency and Dimension of the Firm: Some Results on the Use of Frontier Production Functions," Empirical Economics, Springer, Springer, vol. 2(2), pages 109-22.
  10. Kumbhakar, Subal C., 1997. "Modeling allocative inefficiency in a translog cost function and cost share equations: An exact relationship," Journal of Econometrics, Elsevier, Elsevier, vol. 76(1-2), pages 351-356.
  11. Wang, Hung-Jen, 2003. "A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 21(3), pages 406-19, July.
  12. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 2(4), pages 367-74, October.
  13. Kumbhakar, Subal C. & Wang, Hung-Jen, 2006. "Estimation of technical and allocative inefficiency: A primal system approach," Journal of Econometrics, Elsevier, Elsevier, vol. 134(2), pages 419-440, October.
  14. Hofler, Richard A & Murphy, Kevin J, 1992. "Underpaid and Overworked: Measuring the Effect of Imperfect Information on Wages," Economic Inquiry, Western Economic Association International, Western Economic Association International, vol. 30(3), pages 511-29, July.
  15. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521666633.
  16. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, Elsevier, vol. 9(3), pages 343-366, February.
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