A review of heuristic optimization methods in econometrics
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.
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.SwissFinanceInstitute.ch|
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.:
- Bauer, Dietmar & Wagner, Martin, 2002.
"Estimating cointegrated systems using subspace algorithms,"
Journal of Econometrics,
Elsevier, vol. 111(1), pages 47-84, November.
- Dietmar Bauer & Martin Wagner, 2000. "Estimating Cointegrated Systems Using Subspace Algorithms," Econometric Society World Congress 2000 Contributed Papers 0293, Econometric Society.
- 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.
- Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
- 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.
- Jurgen Doornik & Marius Ooms, 2003.
"Multimodality in the GARCH Regression Model,"
Economics Series Working Papers
2003-W20, University of Oxford, Department of Economics.
- 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.
- 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.
- Winker, Peter, 1994.
"Identification of multivariate AR-models by threshold accepting,"
Discussion Papers, Series II
224, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
- Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 295-307, September.
- Staszewska, Anna, 2007.
"Representing uncertainty about response paths: The use of heuristic optimisation methods,"
Computational Statistics & Data Analysis,
Elsevier, vol. 52(1), pages 121-132, September.
- 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.
- Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
- 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.
- John C. Chao & Peter C.B. Phillips, 1997. "Model Selection in Partially Nonstationary Vector Autoregressive Processes with Reduced Rank Structure," Cowles Foundation Discussion Papers 1155, Cowles Foundation for Research in Economics, Yale University.
- 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.
- 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.
- 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.
- Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
- 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.
- 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.
- 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.
- Chipman, J. & Winker, P., 2005. "Optimal aggregation of linear time series models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 311-331, April.
- 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.
- Michael B. Gordy, . "GA.M: A Matlab routine for function maximization using a Genetic Algorithm," Matlab codes ga, , revised 12 Feb 1996.
- 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.
- 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.
- 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.
- Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
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 references are entirely missing, you can add them using this form.