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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. 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.
  2. ABDELKHALEK, Touhami & DUFOUR, Jean-Marie, 1997. "Statistical Inference for Computable General Equilibrium Models with Application to a Model of the Moroccan Economy," Cahiers de recherche 9713, Universite de Montreal, Departement de sciences economiques.
  3. 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.
  4. Thomas Hertel & David Hummels & Maros Ivanic & Roman Keeney, 2004. "How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements?," NBER Working Papers 10477, National Bureau of Economic Research, Inc.
  5. 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.
  6. Hertel, Thomas, 2013. "Global Applied General Equilibrium Analysis Using the Global Trade Analysis Project Framework," Handbook of Computable General Equilibrium Modeling, Elsevier.
  7. 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.
  8. George Verikios, 2004. "A Model of the World Wool Market," Economics Discussion / Working Papers 04-24, The University of Western Australia, Department of Economics.
  9. Hillberry, Russell & Hummels, David, 2013. "Trade Elasticity Parameters for a Computable General Equilibrium Model," Handbook of Computable General Equilibrium Modeling, Elsevier.
  10. 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.
  11. 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.

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