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Systematic Sensitivity Analysis with Respect to Correlated Variations in Parameters and Shocks

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  • Horridge, Mark
  • Ken Pearson

Abstract

We show how you can carry out systematic sensitivity analysis (SSA) with respect to parameters and/or shocks, which vary according to a specified covariance matrix. You can use the existing SSA tools in RunGTAP or RunGEM to do this if your model is implemented in GEMPACK. Those SSA tools assume that all parameters or shocks are varying independently (i.e., the distributions of all parameters or shocks are uncorrelated) or together (i.e., are completely correlated). The techniques in this paper remove those restrictions. However, users need to make small modifications to the TAB file for the model. Different modifications are needed for different SSA scenarios. Further, the standard SSA procedure built into RunGTAP and RunGEM allows you to compute the sensitivity of model results either with respect to variations in parameter values or with respect to variations in shock values, but you cannot vary both parameters and shocks at the same time. Our discussion concentrates on the parameter case. However, we later show how shock variation may be modelled as a type of parameter variation. This opens the door to simultaneous variation of shocks and parameters. We include worked examples of the techniques described, based on the standard GTAP Model.

Suggested Citation

  • Horridge, Mark & Ken Pearson, 2011. "Systematic Sensitivity Analysis with Respect to Correlated Variations in Parameters and Shocks," GTAP Technical Papers 3496, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    • Handle: RePEc:gta:techpp:3496
    Note: GTAP Technical Paper No. 30
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    File URL: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3496
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    1. T. Chatzivasileiadis & F. Estrada & M. W. Hofkes & R. S. J. Tol, 2019. "Systematic Sensitivity Analysis of the Full Economic Impacts of Sea Level Rise," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1183-1217, March.
    2. Alla A. Golub & Thomas W. Hertel, 2012. "Modeling Land-Use Change Impacts Of Biofuels In The Gtap-Bio Framework," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-30.
    3. Hertel, Thomas, 2013. "Global Applied General Equilibrium Analysis Using the Global Trade Analysis Project Framework," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 815-876, Elsevier.
    4. Magdalena Zachlod-Jelec & Jakub Boratynski, 2016. "How large and uncertain are costs of 2030 GHG emissions reduction target for the European countries? Sensitivity analysis in a global CGE model," MF Working Papers 26, Ministry of Finance in Poland.
    5. James A. Giesecke & Nhi Hoang Tran & Erwin L. Corong & Steven Jaffee, 2013. "Rice Land Designation Policy in Vietnam and the Implications of Policy Reform for Food Security and Economic Welfare," Journal of Development Studies, Taylor & Francis Journals, vol. 49(9), pages 1202-1218, September.
    6. Dandres, Thomas & Gaudreault, Caroline & Seco, Pablo Tirado & Samson, Réjean, 2014. "Uncertainty management in a macro life cycle assessment of a 2005–2025 European bioenergy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 52-61.
    7. Magdalena Zachlod-Jelec & Jakub Boratyński, 2016. "How large and uncertain are costs of 2030 emission reduction target for the European countries? Sensitivity analysis in a global CGE model," EcoMod2016 9449, EcoMod.
    8. Phimister, Euan & Roberts, Deborah, 2014. "Allowing for uncertain and asymmetric policy shocks: a CGE analysis of the impacts of on-shore wind farm developments in north east Scotland," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182663, European Association of Agricultural Economists.

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