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Gaussian Quadratures vs. Monte Carlo Experiments for Systematic Sensitivity Analysis of Computable General Equilibrium Model Results

Author

Listed:
  • Nelson B Villoria

    (Department of Agricultural Economics, Kansas State University)

  • Paul V Preckel

    (Department of Agricultural Economics, Purdue University)

Abstract

Third-order Gaussian quadratures (GQ) approximate the mean and variance of model results allowing for computationally inexpensive sensitivity analysis to uncertainty in exogenous parameters. Unfortunately, commonly used GQ approaches restrict the marginal distributions of both parameters and results sacrificing valuable distributional information. Using higher order quadratures, or incorporating more uncertain exogenous parameters, rapidly increases the sample size, undermining the rationale for using GQ. In contrast, Monte Carlo methods directly approximate the distribution of model outcomes without restrictive distributional assumptions on exogenous parameters. We argue that current computing capabilities allow for wider use of Monte Carlo methods for conducting stochastic simulations.

Suggested Citation

  • Nelson B Villoria & Paul V Preckel, 2017. "Gaussian Quadratures vs. Monte Carlo Experiments for Systematic Sensitivity Analysis of Computable General Equilibrium Model Results," Economics Bulletin, AccessEcon, vol. 37(1), pages 480-487.
    • Handle: RePEc:ebl:ecbull:eb-17-00008
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    References listed on IDEAS

    as
    1. Pearson, Ken & Channing Arndt, 2000. "Implementing Systematic Sensitivity Analysis Using GEMPACK," GTAP Technical Papers 474, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    2. Arndt, Channing, 1996. "An Introduction To Systematic Sensitivity Analysis Via Gaussian Quadrature," Technical Papers 28709, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. 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.
    4. Hertel, Thomas, 1997. "Global Trade Analysis: Modeling and applications," GTAP Books, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, number 7685, December.
    5. Nelson Benjamin Villoria & Elliot Wamboka Mghenyi, 2017. "The Impacts of India's Food Security Policies on South Asian Wheat and Rice Markets," The World Bank Economic Review, World Bank, vol. 31(3), pages 730-746.
    6. Artavia, Marco & Grethe, Harald & Zimmermann, Georg, 2015. "Stochastic market modeling with Gaussian Quadratures: Do rotations of Stroud's octahedron matter?," Economic Modelling, Elsevier, vol. 45(C), pages 155-168.
    7. Jeffrey J. Reimer, 2007. "Assessing Global Computable General Equilibrium Model Validity Using Agricultural Price Volatility," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 383-397.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ziesmer, Johannes & Jin, Ding & Mukashov, Askar & Henning, Christian, 2023. "Integrating fundamental model uncertainty in policy analysis," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    2. 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.
    3. 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.
    4. Theodoros Chatzivasileiadis, 2017. "Quasi-random Monte Carlo application in CGE systematic sensitivity analysis," Papers 1709.09755, arXiv.org.
    5. Tetsuji Tanaka & Jin Guo & Naruto Hiyama & Baris Karapinar, 2022. "Optimality Between Time of Estimation and Reliability of Model Results in the Monte Carlo Method: A Case for a CGE Model," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 151-176, January.
    6. Panknin, Lea & Boy, Karl-Friedrich & Henning, Christian H.C.A., 2024. "Can the European Green Deal be a game changer for sustainable food system transformation? A computational political economy approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343740, Agricultural and Applied Economics Association.
    7. Nelson B. Villoria, 2017. "R Meets GEMPACK for a Monte Carlo Walk," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 2(2), pages 128-154, December.

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    More about this item

    Keywords

    Sampling methods; Gaussian Quadratures; Monte Carlo; Stochastic modeling; Commodity markets;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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