IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc85290.html
   My bibliography  Save this paper

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

Author

Listed:

Abstract

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.

Suggested Citation

  • 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 Working Papers JRC85290, Joint Research Centre (Seville site).
  • Handle: RePEc:ipt:iptwpa:jrc85290
    as

    Download full text from publisher

    File URL: http://publications.jrc.ec.europa.eu/repository/handle/JRC85290
    Download Restriction: no

    References listed on IDEAS

    as
    1. Manuel Alejandro Cardenete & Ferran Sancho, 2004. "Reverse Impact Assessment Using a Regional Social Accounting Matrix," Environment and Planning A, , vol. 36(5), pages 937-945, May.
    2. Arndt, Channing, 1996. "An Introduction to Systematic Sensitivity Analysis via Gaussian Quadrature," GTAP Technical Papers 305, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    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. Gary Gillespie & Peter Mcgregor & J. Kim Swales & Ya Ping Yin, 2001. "The Displacement and Multiplier Effects of Regional Selective Assistance: A Computable General Equilibrium Analysis," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 125-139.
    5. Maureen Kilkenny, 2002. "The New Rural Economy: Discussion," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(5), pages 1253-1255.
    6. Psaltopoulos, Demetris & Balamou, Eudokia & Skuras, Dimitris & Ratinger, Tomas & Sieber, Stefan, 2011. "Modelling the impacts of CAP Pillar 1 and 2 measures on local economies in Europe: Testing a case study-based CGE-model approach," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 53-69, January.
    7. Edward C. Waters & David W. Holland & Bruce A. Weber, 1997. "Economic Impacts of a Property Tax Limitation: A Computable General Equilibrium Analysis of Oregon's Measure 5," Land Economics, University of Wisconsin Press, vol. 73(1), pages 72-89.
    8. Roxana Julia-Wise & Stephen C. Cooke & RDavid Holland, 2002. "A Computable General Equilibrium Analysis of a Property Tax Limitation Initiative in Idaho," Land Economics, University of Wisconsin Press, vol. 78(2), pages 207-227.
    9. Anthony T. Flegg & Timo Tohmo, 2013. "Regional Input--Output Tables and the FLQ Formula: A Case Study of Finland," Regional Studies, Taylor & Francis Journals, vol. 47(5), pages 703-721, May.
    10. David Holland, 2010. "What happens when exports expand: some ideas for closure of regional computable general equilibrium models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(2), pages 439-451, October.
    11. Mark Partridge & Dan Rickman, 2010. "Computable General Equilibrium (CGE) Modelling for Regional Economic Development Analysis," Regional Studies, Taylor & Francis Journals, vol. 44(10), pages 1311-1328.
    12. Gilchrist, Donald A. & St. Louis, Larry V., 1994. "An equilibrium analysis of regional industrial diversification," Regional Science and Urban Economics, Elsevier, vol. 24(1), pages 115-133, February.
    13. Sherman Robinson & Andrea Cattaneo & Moataz El-Said, 2001. "Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods," Economic Systems Research, Taylor & Francis Journals, vol. 13(1), pages 47-64.
    14. Maria Espinosa & Demetrios Psaltopoulos & Fabien Santini & Euan Phimister & Deborah Roberts & Sebastien Mary & Tomas Ratinger & Dimitris Skuras & Eudokia Balamou & Manuel A. Cardenete & Sergio Gomez y, 2014. "Ex-Ante Analysis of the Regional Impacts of the Common Agricultural Policy: A Rural-Urban Recursive Dynamic CGE Model Approach," European Planning Studies, Taylor & Francis Journals, vol. 22(7), pages 1342-1367, July.
    15. Mark D. Partridge & Dan S. Rickman, 1998. "Regional Computable General Equilibrium Modeling: A Survey and Critical Appraisal," International Regional Science Review, , vol. 21(3), pages 205-248, December.
    16. Antoine Belgodere & Charles Vellutini, 2011. "Identifying key elasticities in a CGE model: a Monte Carlo approach," Applied Economics Letters, Taylor & Francis Journals, vol. 18(17), pages 1619-1622.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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, 2017. "Systematic sensitivity analysis of the full economic impacts of sea level rise," Working Paper Series 1617, Department of Economics, University of Sussex.
    3. 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.
    4. 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.

    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:ipt:iptwpa:jrc85290. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Publication Officer). General contact details of provider: http://edirc.repec.org/data/ipjrces.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.