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An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models

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  • Makram El-Shagi

Abstract

We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.

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Bibliographic Info

Paper provided by Halle Institute for Economic Research in its series IWH Discussion Papers with number 1.

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Date of creation: Feb 2010
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Handle: RePEc:iwh:dispap:1-10

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Keywords: evolutionary strategy; genetic algorithm; TVECM;

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References

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  1. Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
  2. 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.
  3. Jamie Gascoigne, 2004. "Estimating threshold vector error-correction models with multiple cointegrating relationships," Working Papers 2004013, The University of Sheffield, Department of Economics, revised Nov 2004.
  4. Manfred GILLI & Peter WINKER, . "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
  5. Balke, Nathan S & Fomby, Thomas B, 1997. "Threshold Cointegration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 627-45, August.
  6. Jaya Krishnakumar & David Neto, 2009. "Estimation and Testing for the Cointegration Rank in a Threshold Cointegrated System," Research Papers by the Department of Economics, University of Geneva 2009.01, Département des Sciences Économiques, Université de Genève.
  7. Qu, Zhongjun & Perron, Pierre, 2007. "A Modified Information Criterion For Cointegration Tests Based On A Var Approximation," Econometric Theory, Cambridge University Press, vol. 23(04), pages 638-685, August.
  8. Doherr, Thorsten & Czarnitzki, Dirk, 2002. "Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications," ZEW Discussion Papers 02-41, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  9. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
  10. Gonzalo, Jesùs & Pitarakis, Jean-Yves, 2005. "Threshold effects In multivariate error correction models," Discussion Paper Series In Economics And Econometrics 0501, Economics Division, School of Social Sciences, University of Southampton.
  11. Hansen, Bruce E. & Seo, Byeongseon, 2002. "Testing for two-regime threshold cointegration in vector error-correction models," Journal of Econometrics, Elsevier, vol. 110(2), pages 293-318, October.
  12. 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.
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Cited by:
  1. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, School of Economics and Management, University of Aarhus.
  2. Schleer, Frauke, 2013. "Finding starting-values for maximum likelihood estimation of vector STAR models," ZEW Discussion Papers 13-076, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

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