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How Reliable Are ORAN I Conclusions?

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  • Pagan, A R
  • Shannon, J H

Abstract

This paper seeks to systematically review the main criticisms of the ORANI model by dev eloping a graphical version of a two-sector (exportables and nonexpor tables) miniature ORANI model. This model shows that ORANI results oc cur because while supply curves in both sectors have similar slopes, the slopes of the demand curves are polar opposites. Furthermore, res ults tend to be more sensitive to variations in supply rather than de mand parameters. Experiments using the ORANI model itself verified th ese findings. Some form of sensitivity analysis with respect to assig ned parameter values should form an integral part of any ORANI experi ment. Copyright 1987 by The Economic Society of Australia.

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Bibliographic Info

Article provided by The Economic Society of Australia in its journal The Economic Record.

Volume (Year): 63 (1987)
Issue (Month): 180 (March)
Pages: 33-45

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Handle: RePEc:bla:ecorec:v:63:y:1987:i:180:p:33-45

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Cited by:
  1. Verikios, George, 2005. "A Model of the World Wool Market," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 139293, Australian Agricultural and Resource Economics Society.
  2. Hertel, Thomas & Hummels, David & Ivanic, Maros & Keeney, Roman, 2007. "How confident can we be of CGE-based assessments of Free Trade Agreements?," Economic Modelling, Elsevier, vol. 24(4), pages 611-635, July.
  3. Robert M´barek & Ivelin Iliev Rizov, 2013. "European Coexistence Bureau. Best Practice Documents for coexistence of genetically modified crops with conventional and organic farming. 3. Coexistence of genetically modified maize and honey product," JRC-IPTS Working Papers JRC84850, Institute for Prospective and Technological Studies, Joint Research Centre.
  4. Touhami Abdelkhalek & Jean-Marie Dufour, 1998. "Statistical Inference For Computable General Equilibrium Models, With Application To A Model Of The Moroccan Economy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 520-534, November.
  5. Axel Tonini & Jerzy Michalek & Thomas Fellmann & Robert M'baretk & Jacques Delincé & George Philippidis & Maciej Bukowski & Piero Conforti & Alexandre Gohin & Andrey Krasovskii & Hans Van Meijl & D, 2013. "Simulating long term effects of policies in the agrifood sector: requirements, challenges and recommendations," Working Papers 222739, Institut National de la Recherche Agronomique, France.
  6. Hillberry, Russell & Hummels, David, 2013. "Trade Elasticity Parameters for a Computable General Equilibrium Model," Handbook of Computable General Equilibrium Modeling, Elsevier.
  7. DeVuyst, Eric A. & Preckel, Paul V., 1997. "Sensitivity analysis revisited: A quadrature-based approach," Journal of Policy Modeling, Elsevier, vol. 19(2), pages 175-185, April.
  8. Joshua Elliott & Meredith Franklin & Ian Foster & Todd Munson & Margaret Loudermilk, 2012. "Propagation of Data Error and Parametric Sensitivity in Computable General Equilibrium Models," Computational Economics, Society for Computational Economics, vol. 39(3), pages 219-241, March.
  9. Michael Malakellis & Matthew Peter, 1991. "Stimulation of Employment in Neo-Classical Models," Centre of Policy Studies/IMPACT Centre Working Papers ip-49, Victoria University, Centre of Policy Studies/IMPACT Centre.
  10. Hertel, Thomas, 2013. "Global Applied General Equilibrium Analysis Using the Global Trade Analysis Project Framework," Handbook of Computable General Equilibrium Modeling, Elsevier.
  11. Rickman, Dan S., 1995. "A bayesian analysis of the use of pooled coefficients in a structural regional economic model," International Journal of Forecasting, Elsevier, vol. 11(3), pages 477-490, September.

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