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Optimal Lag Structure Selection in VEC-Models

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  • Dietmar Maringer
  • Peter Winker

Abstract

For modelling economic and financial time series, multivariate linear and nonlinear systems of equations have become a standard tool. These models can also be applied to non-stationary processes. However, the resulting finite-sample estimates may depend strongly on the specification of the model dynamics. We propose a method for automatic identification of the dynamic part of VEC-models. Model selection is based on a modified information criterion. The lag structure of the model is selected according to this objective function allowing for "holes". The resulting complex discrete optimization problem is tackled using a hybrid heuristic combining ideas from threshold accepting and memetic algorithms. We present the algorithm and the results of a simulation study showing the method's performance both with regard to the dynamic structure and the rank selection in the VEC-model. The results indicate that the selection of the cointregation rank might depend strongly on the specification of the dynamic part of the VEC-model

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 155.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:155

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Keywords: Model selection; cointegration rank; reduced rank regression;

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  1. Jesus Gonzalo & Jean-Yves Pitarakis, 2001. "Lag Length Estimation in Large Dimensional Systems," Econometrics 0108003, EconWPA.
  2. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
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  4. 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.
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  8. Cheung, Yin-Wong & Lai, Kon S, 1993. "Finite-Sample Sizes of Johansen's Likelihood Ration Tests for Conintegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 313-28, August.
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  10. Pötscher, B.M., 1991. "Effects of Model Selection on Inference," Econometric Theory, Cambridge University Press, vol. 7(02), pages 163-185, June.
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  13. Julia Campos & David F. Hendry & Hans-Martin Krolzig, 2003. "Consistent Model Selection by an Automatic "Gets" Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
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  15. Mohsen Bahmani-Oskooee & Taggert Brooks, 2003. "A new criteria for selecting the optimum lags in Johansen's cointegration technique," Applied Economics, Taylor & Francis Journals, vol. 35(8), pages 875-880.
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  17. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  18. Gredenhoff, Mikael & Karlsson, Sune, 1997. "Lag-length Selection in VAR-models Using Equal and Unequal Lag-Length Procedures," Working Paper Series in Economics and Finance 177, Stockholm School of Economics.
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Citations

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Cited by:
  1. Marianna Lyra & Johannes Paha & Sandra Paterlini & Peter Winker, 2009. "Optimization Heuristics for Determining Internal Rating Grading Scales," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 09031, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".
  2. Mezgebo, Taddese, 2009. "A multivariate approach for identification of optimal locations with in Ethiopia’s wheat market to tackle soaring inflation on food price (Extended version)," MPRA Paper 17960, University Library of Munich, Germany.
  3. Manfred GILLI & Peter WINKER, . "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
  4. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Society for Computational Economics, vol. 39(4), pages 337-363, April.
  5. Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
  6. Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.
  7. Mikhail V. Oet & Ryan Eiben & Timothy Bianco & Dieter Gramlich & Stephen J. Ong & Jing Wang, 2011. "SAFE: An early warning system for systemic banking risk," Working Paper 1129, Federal Reserve Bank of Cleveland.
  8. Mezgebo, Taddese, 2009. "A multivariate approach for identification of optimal locations with in Ethiopia’s wheat market to tackle soaring inflation on food price," MPRA Paper 18663, University Library of Munich, Germany.
  9. Badi H. Baltagi & Zijun Wang, 2006. "Testing for Cointegrating Rank via Model Selection: Evidence from 165 Data Sets," Center for Policy Research Working Papers 83, Center for Policy Research, Maxwell School, Syracuse University.

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