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Efficient Sampling and Metamodeling for Computational Economic Models

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

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.

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Paper provided by Groupe de Recherche en Economie Théorique et Appliquée in its series Cahiers du GREThA with number 2012-18.

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Date of creation: 2012
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Handle: RePEc:grt:wpegrt:2012-18
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  1. Nelson, Richard R & Winter, Sidney G, 1982. "The Schumpeterian Tradeoff Revisited," American Economic Review, American Economic Association, vol. 72(1), pages 114-32, March.
  2. Murat YILDIZOGLU (GREQAM, CNRS, UMR 6579) & Marc-Alexandre SENEGAS (GREThA, CNRS, UMR 5113) & Isabelle SALLE (GREThA, CNRS, UMR 5113) & Martin ZUMPE (GREThA, CNRS, UMR 5113), 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Cahiers du GREThA 2011-11, Groupe de Recherche en Economie Théorique et Appliquée.
  3. Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo sampling for DSGE models," Finance and Economics Discussion Series 2013-43, Board of Governors of the Federal Reserve System (U.S.).
  4. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers 10368, Iowa State University, Department of Economics.
  5. 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.
  6. 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.
  7. Walter R. Mebane Jr. & Jasjeet S. Sekhon, . "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, American Statistical Association, vol. 42(i11).
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