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Efficient Sampling and Metamodeling for Computational Economic Models

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  • Murat YILDIZOGLU (GREThA, CNRS, UMR 5113)
  • Isabelle SALLE (GREThA, CNRS, UMR 5113)

Abstract

Extensive exploration of simulation models comes at a high computational cost, all the more when the model involves a lot of parameters. Economists usually rely on random explorations, such as Monte Carlo simulations, and basic econometric modelling to approximate the properties of computational models. This paper aims at providing guidelines for the use of a much more parsimonious method, based on an efficient sampling of the parameters space – a design of experiments (DOE), associated with a well-suited metamodel – kriging. We analyze two simple economic models using this approach to illustrate the possibilities offered by it. Our appendix gives a sample of the R-project code that can be used to apply this method on other models.

Suggested Citation

  • Murat YILDIZOGLU (GREThA, CNRS, UMR 5113) & Isabelle SALLE (GREThA, CNRS, UMR 5113), 2012. "Efficient Sampling and Metamodeling for Computational Economic Models," Cahiers du GREThA 2012-18, Groupe de Recherche en Economie Théorique et Appliquée.
  • Handle: RePEc:grt:wpegrt:2012-18
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    File URL: http://cahiersdugretha.u-bordeaux4.fr/2012/2012-18.pdf
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    References listed on IDEAS

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    1. Yıldızoğlu, Murat & Sénégas, Marc-Alexandre & Salle, Isabelle & Zumpe, Martin, 2014. "Learning The Optimal Buffer-Stock Consumption Rule Of Carroll," Macroeconomic Dynamics, Cambridge University Press, pages 727-752.
    2. Nelson, Richard R & Winter, Sidney G, 1982. "The Schumpeterian Tradeoff Revisited," American Economic Review, American Economic Association, pages 114-132.
    3. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    4. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    5. Mebane Jr., Walter R. & Sekhon, Jasjeet S., 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, Foundation for Open Access Statistics.
    6. Oeffner, Marc, 2008. "Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation," MPRA Paper 18199, University Library of Munich, Germany, revised Oct 2009.
    7. Richard R. Nelson & Sidney G. Winter, 1978. "Forces Generating and Limiting Concentration under Schumpeterian Competition," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 524-548, Autumn.
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    Cited by:

    1. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-1.
    2. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-1.
    3. repec:eee:dyncon:v:82:y:2017:i:c:p:125-141 is not listed on IDEAS
    4. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Sciences Po publications 2017-09, Sciences Po.
    5. Celine Bonnefond, 2014. "Growth Dynamics And Conditional Convergence Among Chinese Provinces: A Panel Data Investigation Using System Gmm Estimator," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 39(4), pages 1-25, December.
    6. Dosi, Giovanni & Pereira, Marcelo C. & Roventini, Andrea & Virgillito, Maria Enrica, 2017. "Causes and Consequences of Hysteresis: Aggregate Demand, Productivity and Employment," GLO Discussion Paper Series 64, Global Labor Organization (GLO).
    7. Mattia Guerini & Alessio Moneta, "undated". "A Method for Agent-Based Models Validation," Working Papers Series 42, Institute for New Economic Thinking.
    8. Barde, Sylvain, 2016. "Direct comparison of agent-based models of herding in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 329-353.
    9. Caiani, Alessandro & Russo, Alberto & Gallegati, Mauro, 2017. "Are higher wages good for business? An assessment under alternative innovation and investment scenarios," MPRA Paper 80439, University Library of Munich, Germany.
    10. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, pages 125-141.
    11. Gerard Ballot & Antoine Mandel & Annick Vignes, 2015. "Agent-based modeling and economic theory: where do we stand?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, pages 199-220.
    12. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Studies in Economics 1712, School of Economics, University of Kent.
    13. Hazan, Aurélien, 2017. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 589-602.
    14. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Gerard Ballot & Antoine Mandel & Annick Vignes, 2015. "Agent-based modeling and economic theory: where do we stand?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, pages 199-220.
    16. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    17. Giovanni Dosi & Marcelo C. Pereira & Maria Enrica Virgillito, 2016. "On the robustness of the fat-tailed distribution of firm growth rates: a global sensitivity analysis," LEM Papers Series 2016/12, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    More about this item

    Keywords

    Computational Economics; Exploration of Agent-Based Models; Design of Experiments; Metamodeling;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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