Estimating State-Contingent Production Frontiers
Chambers and Quiggin (2000) advocate the use of state-contingent production technologies to represent risky production and establish important theoretical results concerning producer behaviour under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to standard estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies and other quantities of economic interest.
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- Newbery, David M, 1989. "The Theory of Food Price Stabilisation," Economic Journal, Royal Economic Society, vol. 99(398), pages 1065-82, December.
- Chambers, R.G. & Quiggin, J., 1995.
"Separation and Hedging Results with State-Contingent Production,"
293, Australian National University - Department of Economics.
- Chambers, Robert G & Quiggin, John, 1997. "Separation and Hedging Results with State-Contingent Production," Economica, London School of Economics and Political Science, vol. 64(254), pages 187-209, May.
- Rasmussen, Svend, 2004. "Optimizing Production under Uncertainty: Generalisation of the State-Contingent Approach and Comparison of Methods for Empirical Application," Unit of Economics Working papers 24184, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.
- Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
- Chambers, Robert G. & Quiggin, John, 1996. "Non-point-source pollution regulation as a multi-task principal-agent problem," Journal of Public Economics, Elsevier, vol. 59(1), pages 95-116, January.
- Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
- repec:cup:cbooks:9780521785235 is not listed on IDEAS
- Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-32.
- Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
- Robert G. Chambers & John Quiggin, 2002. "The State-Contingent Properties of Stochastic Production Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 513-526.
- Chambers, Robert G. & Quiggin, John C., 2004.
"Technological and financial approaches to risk management in agriculture: an integrate approach,"
Australian Journal of Agricultural and Resource Economics,
Australian Agricultural and Resource Economics Society, vol. 48(2), June.
- Robert G. Chambers & John Quiggin, 2004. "Technological and financial approaches to risk management in agriculture: an integrated approach ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), pages 199-223, 06.
- 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, vol. 38(3), pages 387-399, July.
- 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.
- KOOP, Gary & STEEL, MarkÂ F. & OSIEWALSKI, Jacek, 1994. "Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling," CORE Discussion Papers 1994061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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