Journal of Productivity Analysis

, Volume 41, Issue 1, pp 131–140 | Cite as

Technical efficiency with state-contingent production frontiers using maximum entropy estimators

Article

Abstract

Although the theory of state-contingent production is well-established, the empirical implementation of this approach is still in an infancy stage. The possibility of finding a large number of states of nature, few observations per state and models affected by collinearity have led some researchers to claim the urgent need to develop robust estimation techniques. In this paper, we investigate the performance of some maximum entropy estimators to assess technical efficiency with state-contingent production frontiers. The methodological discussion and the simulation study provided in the paper reveal some of the potential of these estimators. Small mean squared error loss and small differences between the true and the estimated mean of technical efficiency show that the maximum entropy can be a powerful tool in the estimation of state-contingent production frontiers.

Keywords

Maximum entropy State-contingent production Technical efficiency 

JEL Classification

C13 C15 

References

  1. Campbell R, Rogers K, Rezek J (2008) Efficient frontier estimation: a maximum entropy approach. J Prod Anal 30:213–221CrossRefGoogle Scholar
  2. Chambers RG, Quiggin J (2000) Uncertainty, production, choice, and agency: the state-contingent approach. Cambridge University Press, CambridgeGoogle Scholar
  3. Chambers RG, Quiggin J (2002) The state-contingent properties of stochastic production functions. Am J Agric Econ 84:513–526CrossRefGoogle Scholar
  4. Chambers RG, Quiggin J (2007) Dual approaches to the analysis of risk aversion. Economica 74:189–213CrossRefGoogle Scholar
  5. Chavas JP (2008) A cost approach to economic analysis under state-contingent production uncertainty. Am J Agric Econ 90:435–446CrossRefGoogle Scholar
  6. Golan A, Perloff JM (2002) Comparison of maximum entropy and higher-order entropy estimators. J Econom 107:195–211CrossRefGoogle Scholar
  7. Golan A, Judge G, Miller D (1996) Maximum entropy econometrics: robust estimation with limited data. Wiley, ChichesterGoogle Scholar
  8. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, CambridgeGoogle Scholar
  9. Nauges C, O’Donnell C, Quiggin J (2009) Uncertainty and technical efficiency in Finnish agriculture. In: Australian agricultural and resource economics society—53rd annual conference, Cairns, North Queensland, AustraliaGoogle Scholar
  10. O’Donnell CJ, Griffiths WE (2006) Estimating state-contingent production frontiers. Am J Agric Econ 88:249–266CrossRefGoogle Scholar
  11. O’Donnell CJ, Chambers RG, Quiggin J (2010) Efficiency analysis in the presence of uncertainty. J Prod Anal 33:1–17CrossRefGoogle Scholar
  12. Quiggin J, Chambers RG (2006) The state-contingent approach to production under uncertainty. Aust J Agric Resour Econ 50:153–169CrossRefGoogle Scholar
  13. Rasmussen S (2003) Criteria for optimal production under uncertainty. the state-contingent approach. Aust J Agric Resour Econ 47:447–476CrossRefGoogle Scholar
  14. Rasmussen S, Karantininis K (2005) Estimating state-contingent production functions. In: European Association of Agricultural Economists—XIth conference, Copenhagen, DenmarkGoogle Scholar
  15. Rényi A (1970) Probability theory. North-Holland Publishing Company, AmsterdamGoogle Scholar
  16. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423CrossRefGoogle Scholar
  17. Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. J Stat Phys 52:479–487CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Center for Research and Development in Mathematics and Applications, Department of MathematicsUniversity of AveiroAveiroPortugal
  2. 2.CEF.UP, Faculty of EconomicsUniversity of PortoPortoPortugal

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