Efficient Sampling and Metamodeling for Computational Economic Models

Murat Yildizoglu and Isabelle Salle

Cahiers du GREThA (2007-2019) from Groupe de Recherche en Economie Théorique et Appliquée (GREThA)

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.

Keywords: Computational Economics; Exploration of Agent-Based Models; Design of Experiments; Metamodeling (search for similar items in EconPapers)
JEL-codes: C61 C63 C80 C90 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Efficient Sampling and Meta-Modeling for Computational Economic Models (2014)
Working Paper: Efficient Sampling and Metamodeling for Computational Economic Models (2014)
Working Paper: Efficient sampling and metamodeling for computation economic models (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:grt:wpegrt:2012-18

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