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Optimized Multivariate Lag Structure Selection

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  • Peter Winker

Abstract

Model selection – choosing the relevant variables and structures – is a central task in econometrics. Given a limited number of observations, estimation and inference depend on this choice. A frequently treated model-selection problem arises in multivariate autoregressive models, where the problem reduces to the choice of a dynamic structure. In most applications this choice is based either on some ad hoc procedure or on a search within a very small subset of all possible models. In this paper the selection is performed using an explicit optimization approach for a given information criterion. Since complete enumeration of all possible lag structures is infeasible even for moderate dimensions, the global optimization heuristic of threshold accepting is implemented. A simulation study compares this approach with the standard ’take all up to the kth lag‘ approach. It is found that, if the lag structure of the true model is sparse, the threshold accepting optimization approach gives far better approximations.

Suggested Citation

  • Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
  • Handle: RePEc:kap:compec:v:16:y:2000:i:1/2:p:87-103
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    Citations

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    Cited by:

    1. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    2. Chipman, John Somerset & Winker, Peter, 1994. "Optimal industrial classification: [an application to the German industrial classification system]," Discussion Papers, Series II 236, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    3. Chipman, J. & Winker, P., 2005. "Optimal aggregation of linear time series models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 311-331, April.
    4. 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.
    5. 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.
    6. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    7. Gatu, Cristian & Kontoghiorghes, Erricos J., 2006. "Estimating all possible SUR models with permuted exogenous data matrices derived from a VAR process," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 721-739, May.
    8. Grigori Fainstein & Igor Novikov, 2011. "The role of macroeconomic determinants in credit risk measurement in transition country: Estonian example," International Journal of Transitions and Innovation Systems, Inderscience Enterprises Ltd, vol. 1(2), pages 117-137.
    9. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
    10. Manfred GILLI & Peter WINKER, "undated". "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.

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