Efficient Sampling and Metamodeling for Computational Economic Models
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.
|Date of creation:||2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +33 (0)22.214.171.124.75
Fax: +33 (0)126.96.36.199.47
Web page: http://gretha.u-bordeaux4.fr/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Edward Herbst & Frank Schorfheide, 2012. "Sequential Monte Carlo sampling for DSGE models," Working Papers 12-27, Federal Reserve Bank of Philadelphia.
- 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.).
- Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo Sampling for DSGE Models," NBER Working Papers 19152, National Bureau of Economic Research, Inc.
- 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).
- 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.
- Yıldızoğlu, Murat & Sénégas, Marc-Alexandre & Salle, Isabelle & Zumpe, Martin, 2014. "Learning The Optimal Buffer-Stock Consumption Rule Of Carroll," Macroeconomic Dynamics, Cambridge University Press, vol. 18(04), pages 727-752, June.
- Murat Yildizoglu & Marc-Alexandre Sénégas & Isabelle Salle & Martin Zumpe, 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Working Papers halshs-00573689, HAL.
- 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.
- 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.
- Nelson, Richard R & Winter, Sidney G, 1982. "The Schumpeterian Tradeoff Revisited," American Economic Review, American Economic Association, vol. 72(1), pages 114-32, March.
- 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.
When requesting a correction, please mention this item's handle: RePEc:grt:wpegrt:2012-18. 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: (Emmanuel Petit)
If references are entirely missing, you can add them using this form.