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A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results

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  • Sébastien Mary
  • Euan Phimister
  • Deborah Roberts
  • Fabien Santini

Abstract

Parameter uncertainty has fuelled criticisms on the robustness of results from computable general equilibrium models. This has led to the development of alternative sensitivity analysis approaches. Researchers have used Monte Carlo analysis for systematic sensitivity analysis because of its flexibility. But Monte Carlo analysis may yield biased simulation results. Gaussian quadratures have also been widely applied, although they can be difficult to apply in practice. This paper applies an alternative approach to systematic sensitivity analysis, Monte Carlo filtering and examines how its results compare to both Monte Carlo and Gaussian quadrature approaches. It does so via an application to rural development policies in Aberdeenshire, Scotland. We find that Monte Carlo filtering outperforms the conventional Monte Carlo approach and is a viable alternative when a Gaussian quadrature approach cannot be applied or is too complex to implement.

Suggested Citation

  • Sébastien Mary & Euan Phimister & Deborah Roberts & Fabien Santini, 2019. "A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results," Economic Systems Research, Taylor & Francis Journals, vol. 31(3), pages 404-422, July.
    • Handle: RePEc:taf:ecsysr:v:31:y:2019:i:3:p:404-422
    DOI: 10.1080/09535314.2018.1543182
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    Cited by:

    1. Davit Stepanyan & Harald Grethe & Khalid Siddig, 2019. "Comment on "A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results"," Economics Bulletin, AccessEcon, vol. 39(3), pages 1925-1929.
    2. Korrakot Phomsoda & Nattapong Puttanapong & Mongkut Piantanakulchai, 2021. "Economic Impacts of Thailand’s Biofuel Subsidy Reallocation Using a Dynamic Computable General Equilibrium (CGE) Model," Energies, MDPI, vol. 14(8), pages 1-21, April.
    3. Korrakot Phomsoda & Nattapong Puttanapong & Mongkut Piantanakulchai, 2021. "Assessing Economic Impacts of Thailand’s Fiscal Reallocation between Biofuel Subsidy and Transportation Investment: Application of Recursive Dynamic General Equilibrium Model," Energies, MDPI, vol. 14(14), pages 1-32, July.
    4. Britz, Wolfgang & Li, Jingwen & Shang, Linmei, 2021. "Combining large-scale sensitivity analysis in Computable General Equilibrium models with Machine Learning: An Example Application to policy supporting the bio-economy," Conference papers 333285, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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