IDEAS home Printed from
MyIDEAS: Login to save this paper or follow this series

A review of heuristic optimization methods in econometrics

  • Manfred GILLI

    (University of Geneva and Swiss Finance Institute)

  • Peter WINKER

    (University of Giessen)

Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as evolutionary algorithms (Simulated Annealing, Threshold Accepting), Neural Networks, Genetic Algorithms, Tabu Search, hybrid methods and many others, which have been developed over the last two decades. Although the use of these methods became more standard in several fields of sciences, their use in estimation and modelling in econometrics appears to be still limited. We present an introduction to heuristic optimization methods and provide some examples for which these methods are found to work efficiently.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 08-12.

in new window

Length: 47 pages
Date of creation:
Date of revision:
Handle: RePEc:chf:rpseri:rp0812
Contact details of provider: Web page:

More information through EDIRC

References listed on IDEAS
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.:

as in new window
  1. S. P. Brooks & N. Friel & R. King, 2003. "Classical model selection via simulated annealing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 503-520.
  2. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
  3. Fitzenberger, Bernd & Winker, Peter, 1999. "Improving the Computation of Censored Quantile Regressions," Discussion Papers 568, Institut fuer Volkswirtschaftslehre und Statistik, Abteilung fuer Volkswirtschaftslehre.
  4. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
  5. Bauer, Dietmar & Wagner, Martin, 2002. "Estimating cointegrated systems using subspace algorithms," Journal of Econometrics, Elsevier, vol. 111(1), pages 47-84, November.
  6. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
  7. Anna Staszewska, 2006. "Representing Uncertainty about Response Paths: the Use of Heuristic Optimisation Methods," Computing in Economics and Finance 2006 379, Society for Computational Economics.
  8. Kapetanios, George, 2006. "Choosing the optimal set of instruments from large instrument sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 612-620, November.
  9. Kwami Adanu, 2006. "Optimizing the Garch Model–An Application of Two Global and Two Local Search Methods," Computational Economics, Society for Computational Economics, vol. 28(3), pages 277-290, October.
  10. Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 295-307, September.
  11. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
  12. Alcock, Jamie & Burrage, Kevin, 2004. "A genetic estimation algorithm for parameters of stochastic ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 255-275, September.
  13. Baragona, Roberto & Battaglia, Francesco & Calzini, Claudio, 2001. "Genetic algorithms for the identification of additive and innovation outliers in time series," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 1-12, July.
  14. Winker, Peter & Maringer, Dietmar, 2005. "The convergence of optimization based estimators : theory and application to a GARCH-model," Discussion Papers 2005,004E, University of Erfurt, Faculty of Economics, Law and Social Sciences.
  15. Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2007. "GSA-based maximum likelihood estimation for threshold vector error correction model," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 109-120, September.
  16. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
  17. Baragona, R. & Battaglia, F. & Cucina, D., 2004. "Fitting piecewise linear threshold autoregressive models by means of genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 277-295, September.
  18. Winker, Peter & Fang, Kai-Tai, 1995. "Application of threshold accepting to the evaluation of the discrepancy of a set of points," Discussion Papers, Series II 248, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
  19. Maddala, G S & Nelson, Forrest D, 1974. "Maximum Likelihood Methods for Models of Markets in Disequilibrium," Econometrica, Econometric Society, vol. 42(6), pages 1013-30, November.
  20. Chao, John C. & Phillips, Peter C. B., 1999. "Model selection in partially nonstationary vector autoregressive processes with reduced rank structure," Journal of Econometrics, Elsevier, vol. 91(2), pages 227-271, August.
  21. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2007. "Model selection via genetic algorithms illustrated with cross-country growth data," Empirical Economics, Springer, vol. 33(2), pages 313-337, September.
  22. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
  23. Chipman, J. & Winker, P., 2005. "Optimal aggregation of linear time series models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 311-331, April.
  24. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
  25. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp0812. 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: (Marilyn Barja)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.