IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v59y2022i1d10.1007_s10614-020-10080-8.html
   My bibliography  Save this article

Optimality Between Time of Estimation and Reliability of Model Results in the Monte Carlo Method: A Case for a CGE Model

Author

Listed:
  • Tetsuji Tanaka

    (Setsunan University)

  • Jin Guo

    (Setsunan University)

  • Naruto Hiyama

    (Funai Corporation, Inc)

  • Baris Karapinar

    (Boğaziçi University)

Abstract

Computable general equilibrium (CGE) is one of the most frequently utilised macroeconomic models in policy decision-making processes. Economists introduced a stochastic concept to deterministic CGE models using the Monte Carlo (MC) method to identify the effects of climate change or extreme weather patterns that have exacerbated global food insecurity. However, a weakness of the MC method is its time-consuming process to approximate probability distributions with a considerable number of randomised draws. Modellers have unavoidably to face a trade-off between the duration of computation and the accuracy of a model’s results. This paper explores an optimal balance point between the two elements in CGE analysis. Assuming that 2000 repetitive simulations create adequately precise simulation outcomes, we compare model results from 100, 500 and 1000 iterations with those from 2000 repetitive calculations. We found that 1000-time iterations indicate highly credible outcomes, 500-time simulations can function well; however, with moderate accuracy, whereas 100-time calculations are apparently insufficient to obtain reliable outcomes.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10080-8
    DOI: 10.1007/s10614-020-10080-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-020-10080-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-020-10080-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Gözde Erhan Ünal & Baris Karapinar & Tetsuji Tanaka, 2018. "Welfare†at†Risk and Extreme Dependency of Regional Wheat Yields: Implications of a Stochastic CGE Model," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(1), pages 18-34, February.
    3. Devaragan, Shantayanan & Lewis, Jeffrey D. & Robinson, Sherman, 1990. "Policy lessons from trade-focused, two-sector models," Journal of Policy Modeling, Elsevier, vol. 12(4), pages 625-657.
    4. David Abler & Adrián Rodríguez & James Shortle, 1999. "Parameter Uncertainty in CGE Modeling of the Environmental Impacts of Economic Policies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 14(1), pages 75-94, July.
    5. Devarajan, Shantayanan & Lewis, Jeffrey & Robinson, Sherman, 1990. "Policy Lessons from Two-Sector Models," CUDARE Working Papers 198566, University of California, Berkeley, Department of Agricultural and Resource Economics.
    6. 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.
    7. 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.
    8. Seale, James L., Jr. & Regmi, Anita & Bernstein, Jason, 2003. "International Evidence On Food Consumption Patterns," Technical Bulletins 33580, United States Department of Agriculture, Economic Research Service.
    9. Tanaka, Tetsuji & Hosoe, Nobuhiro, 2011. "Does agricultural trade liberalization increase risks of supply-side uncertainty?: Effects of productivity shocks and export restrictions on welfare and food supply in Japan," Food Policy, Elsevier, vol. 36(3), pages 368-377, June.
    10. Sassi, Maria & Cardaci, Alberto, 2013. "Impact of rainfall pattern on cereal market and food security in Sudan: Stochastic approach and CGE model," Food Policy, Elsevier, vol. 43(C), pages 321-331.
    11. Seale, James L., Jr. & Regmi, Anita & Bernstein, Jason, 2003. "International Evidence On Food Consumption Patterns," Technical Bulletins 33580, United States Department of Agriculture, Economic Research Service.
    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. Sulamaa, Pekka & Widgrén, Mika, 2005. "Asian Regionalism versus Global Free Trade: A Simulation Study on Economic Effects," Discussion Papers 985, The Research Institute of the Finnish Economy.
    2. Tetsuji Tanaka & Jin Guo, 2020. "Realistic Specifications and Model Predictability: Testing the Performance of a Stochastic CGE Model with Regionally Correlated Yield Variability in the Wheat and Rice Sectors," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 8(1), pages 55-75, March.
    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. Mario Holzner, 2004. "GSIM Measurement of the Costs of Protection in Southeast Europe," wiiw Balkan Observatory Working Papers 55, The Vienna Institute for International Economic Studies, wiiw.
    5. Zachlod-Jelec, Magdalena & Boratynski, Jakub, 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.
    6. Abdoul G. Sam & Babatunde O. Abidoye & Sihle Mashaba, 2021. "Climate change and household welfare in sub-Saharan Africa: empirical evidence from Swaziland," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(2), pages 439-455, April.
    7. E.A. Haddad & J. Bonet & G.J.D. Hewings & F.S. Perobelli, 2009. "Spatial aspects of trade liberalization in Colombia: A general equilibrium approach," Papers in Regional Science, Wiley Blackwell, vol. 88(4), pages 699-732, November.
    8. Kym Anderson & Ernesto Valenzuela & Lee Ann Jackson, 2008. "Recent and Prospective Adoption of Genetically Modified Cotton: A Global Computable General Equilibrium Analysis of Economic Impacts," Economic Development and Cultural Change, University of Chicago Press, vol. 56(2), pages 265-296, January.
    9. Seale, James L. & Solano, Alexis A., 2012. "The changing demand for energy in rich and poor countries over 25years," Energy Economics, Elsevier, vol. 34(6), pages 1834-1844.
    10. Rahul Anand & Mr. Eswar S Prasad, 2010. "Optimal Price Indices for Targeting Inflation Under Incomplete Markets," IMF Working Papers 2010/200, International Monetary Fund.
    11. Hertel, Thomas W. & Tyner, Wallace E. & Birur, Dileep K., 2008. "Biofuels for all? Understanding the Global Impacts of Multinational Mandates," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6526, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Abbott, Philip & Bentzen, Jeanet & Huong, Thi Lan & Tarp, Finn, 2007. "A Critical Review of Studies on the Social and Economic Impacts of Vietnam’s International Economic Integration," MPRA Paper 29789, University Library of Munich, Germany.
    13. Beaman, Lori & Dillon, Andrew, 2012. "Do household definitions matter in survey design? Results from a randomized survey experiment in Mali," Journal of Development Economics, Elsevier, vol. 98(1), pages 124-135.
    14. Hermeling, Claudia & Löschel, Andreas & Mennel, Tim, 2013. "A new robustness analysis for climate policy evaluations: A CGE application for the EU 2020 targets," Energy Policy, Elsevier, vol. 55(C), pages 27-35.
    15. Gordon Menzies & David Vines, 2008. "The Transfer Problem and Real Exchange Rate Overshooting in Financial Crises: The Role of the Debt Servicing Multiplier," Review of International Economics, Wiley Blackwell, vol. 16(4), pages 709-727, September.
    16. McDonald, Scott & Robinson, Sherman & Thierfelder, Karen, 2008. "Asian Growth and Trade Poles: India, China, and East and Southeast Asia," World Development, Elsevier, vol. 36(2), pages 210-234, February.
    17. Hertel, Thomas & Hummels, David & Ivanic, Maros & Keeney, Roman, 2007. "How confident can we be of CGE-based assessments of Free Trade Agreements?," Economic Modelling, Elsevier, vol. 24(4), pages 611-635, July.
    18. Lewis, Jeffrey D. & Robinson, Sherman & Wang, Zhi, 1995. "Beyond the Uruguay Round: The implications of an Asian free trade area," China Economic Review, Elsevier, vol. 6(1), pages 35-90.
    19. Jean-Michel Courtault & Riccardo Magnani, 2014. "How much can European governments squeeze out of their taxpayers?," Economics Bulletin, AccessEcon, vol. 34(3), pages 1945-1960.
    20. Patrick Blagrave & Peter Elliott & Mr. Roberto Garcia-Saltos & Mr. Douglas Hostland & Mr. Douglas Laxton & Fan Zhang, 2013. "Adding China to the Global Projection Model," IMF Working Papers 2013/256, International Monetary Fund.

    More about this item

    Keywords

    Monte Carlo method; Computable general equilibrium model; Agricultural productivity; Stochastic model;
    All these keywords.

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • Q17 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agriculture in International Trade
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    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:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10080-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.