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Efficient Sampling and Metamodeling for Computational Economic Models

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Author Info

  • 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.

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Bibliographic Info

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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Related research

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

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  1. Murat Yildizoglu & Marc-Alexandre Sénégas & Isabelle Salle & Martin Zumpe, 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Working Papers halshs-00573689, HAL.
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Cited by:
  1. Isabelle SALLE & Marc-Alexandre SENEGAS & Murat YILDIZOGLU, 2013. "How Transparent About Its Inflation Target Should a Central Bank be? An Agent-Based Model Assessment," Cahiers du GREThA 2013-24, Groupe de Recherche en Economie Théorique et Appliquée.

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