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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.

Suggested Citation

  • Pagan, A R & Shannon, J H, 1987. "How Reliable Are ORAN I Conclusions?," The Economic Record, The Economic Society of Australia, vol. 63(180), pages 33-45, March.
  • 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. 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. Y. Qiang, 1999. "CGE Modelling and Australian Economics," Economics Discussion / Working Papers 99-04, The University of Western Australia, Department of Economics.
    4. 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.
    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, Springer;Society for Computational Economics, vol. 39(3), pages 219-241, March.
    6. George Verikios, 2004. "A Model of the World Wool Market," Economics Discussion / Working Papers 04-24, The University of Western Australia, Department of Economics.
    7. Robert MĀ“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 Working Papers JRC84850, Joint Research Centre (Seville site).
    8. 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.
    9. Hertel, Thomas, 2013. "Global Applied General Equilibrium Analysis Using the Global Trade Analysis Project Framework," Handbook of Computable General Equilibrium Modeling, Elsevier.
    10. Hillberry, Russell & Hummels, David, 2013. "Trade Elasticity Parameters for a Computable General Equilibrium Model," Handbook of Computable General Equilibrium Modeling, Elsevier.
    11. 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.

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