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Testing the sensitivity of CGE results: A Monte Carlo Filtering approach to an application to rural development policies in Aberdeenshire

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Parameter uncertainty has fuelled criticisms on the robustness of CGE results and has led to the development of alternative approaches to sensitivity analyses. Researchers have used Monte Carlo (MC) for systematic sensitivity analysis (SSA) because of its flexibility. However, MC may provide biased simulation results. Gaussian Quadratures (GQ) have then been developed, but they are much more difficult to apply in practical modelling and may not always be desirable. This report applies an alternative approach to SSA, Monte Carlo Filtering, and examines how its results compare to MC and GQ approaches, in an application to rural development policies in Aberdeenshire.

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  • Sébastien Mary & Euan Phimister & Deborah Roberts & Fabien Santini, 2013. "Testing the sensitivity of CGE results: A Monte Carlo Filtering approach to an application to rural development policies in Aberdeenshire," JRC Research Reports JRC85290, Joint Research Centre.
    • Handle: RePEc:ipt:iptwpa:jrc85290
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    1. Matthias Weitzel, 2017. "The role of uncertainty in future costs of key CO2 abatement technologies: a sensitivity analysis with a global computable general equilibrium model," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(1), pages 153-173, January.
    2. 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.
    3. Kim, Euijune & Hewings, Geoffrey J.D. & Lee, Changkeun, 2016. "Impact of educational investments on economic losses from population ageing using an interregional CGE-population model," Economic Modelling, Elsevier, vol. 54(C), pages 126-138.
    4. Antimiani, Alessandro & Costantini, Valeria & Paglialunga, Elena, 2015. "The sensitivity of climate-economy CGE models to energy-related elasticity parameters: Implications for climate policy design," Economic Modelling, Elsevier, vol. 51(C), pages 38-52.
    5. Alessandro Antimiani & Valeria Costantini & Elena Paglialunga, 2015. "An analysis of the sensitivity of a dynamic climate-economy CGE model (GDynE) to empirically estimated energy-related elasticity parameters," SEEDS Working Papers 0515, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2015.
    6. Li, Xin, 2014. "The Demographic Structure and Export Strategy in Emerging Economies," Conference papers 332502, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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