IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1709.09755.html
   My bibliography  Save this paper

Quasi-random Monte Carlo application in CGE systematic sensitivity analysis

Author

Listed:
  • Theodoros Chatzivasileiadis

Abstract

The uncertainty and robustness of Computable General Equilibrium models can be assessed by conducting a Systematic Sensitivity Analysis. Different methods have been used in the literature for SSA of CGE models such as Gaussian Quadrature and Monte Carlo methods. This paper explores the use of Quasi-random Monte Carlo methods based on the Halton and Sobol' sequences as means to improve the efficiency over regular Monte Carlo SSA, thus reducing the computational requirements of the SSA. The findings suggest that by using low-discrepancy sequences, the number of simulations required by the regular MC SSA methods can be notably reduced, hence lowering the computational time required for SSA of CGE models.

Suggested Citation

  • Theodoros Chatzivasileiadis, 2017. "Quasi-random Monte Carlo application in CGE systematic sensitivity analysis," Papers 1709.09755, arXiv.org.
    • Handle: RePEc:arx:papers:1709.09755
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1709.09755
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Theodoros N. Chatzivasileiadis & Marjan W. Hofkes & Onno J. Kuik & Richard S.J. Tol, 2016. "Full economic impacts of sea level rise: loss of productive resources and transport disruptions," Working Paper Series 09916, Department of Economics, University of Sussex Business School.
    3. 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.
    4. Jank, Wolfgang, 2005. "Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 685-701, April.
    5. Theodoros N. Chatzivasileiadis & Marjan W. Hofkes & Onno J. Kuik & Richard S.J. Tol, 2016. "Full economic impacts of sea level rise: loss of productive resources and transport disruptions," Working Paper Series 9916, Department of Economics, University of Sussex.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Ziesmer, Johannes & Jin, Ding & Mukashov, Askar & Henning, Christian, 2023. "Integrating fundamental model uncertainty in policy analysis," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    3. Tariqur Rahman Bhuiyan & Ah Choy Er & Nurfashareena Muhamad & Joy Jacqueline Pereira, 2021. "The socioeconomic impact of climate-related hazards: flash flood impact assessment in Kuala Lumpur, Malaysia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1509-1538, November.
    4. Nina Knittel & Martin W. Jury & Birgit Bednar-Friedl & Gabriel Bachner & Andrea K. Steiner, 2020. "A global analysis of heat-related labour productivity losses under climate change—implications for Germany’s foreign trade," Climatic Change, Springer, vol. 160(2), pages 251-269, May.
    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. Theodoros Chatzivasileiadis & Ignasi Cortes Arbues & Daniel Lincke & Jochen Hinkel & Theodoros Chatzivasileiadis & Richard S.J. Tol, "undated". "Actualised and future changes in regional economic growth through sea level rise," Working Paper Series 0324, Department of Economics, University of Sussex Business School.
    8. Gonzalez, Jorge & Tuerlinckx, Francis & De Boeck, Paul & Cools, Ronald, 2006. "Numerical integration in logistic-normal models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1535-1548, December.
    9. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
    10. Chen, Yunxia & Zhang, Wenbo & Xu, Dan, 2019. "Reliability assessment with varying safety threshold for shock resistant systems," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 49-60.
    11. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    12. Johannes Ziesmer, 2024. "Identifying key sectors of sustainable development: A Bayesian framework estimating policy‐impacts in a general equilibrium," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 458-483, April.
    13. 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.
    14. Mukashov, A., 2023. "Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 187-202.
    15. 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.
    16. F. Y. Kuo & W. T. M. Dunsmuir & I. H. Sloan & M. P. Wand & R. S. Womersley, 2008. "Quasi-Monte Carlo for Highly Structured Generalised Response Models," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 239-275, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1709.09755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.