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Optimal Lag Structure Selection in VEC-Models


  • Dietmar Maringer
  • Peter Winker


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

Suggested Citation

  • Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:155

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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    4. Oet, Mikhail V. & Bianco, Timothy & Gramlich, Dieter & Ong, Stephen J., 2013. "SAFE: An early warning system for systemic banking risk," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4510-4533.
    5. Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010. "Optimization heuristics for determining internal rating grading scales," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2693-2706, November.
    6. Badi Baltagi & Zijun Wang, 2007. "Testing for Cointegrating Rank Via Model Selection: Evidence From 165 Data Sets," Empirical Economics, Springer, vol. 33(1), pages 41-49, July.
    7. 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.
    8. José Antonio Gibanel Salazar, 2014. "Economic models: comparative analysis of their adjustment and prediction capacities," Contribuciones a la Economía, Grupo (Universidad de Málaga), issue 2014-05, November.
    9. Manfred GILLI & Peter WINKER, "undated". "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
    10. 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.

    More about this item


    Model selection; cointegration rank; reduced rank regression;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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