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Identification of multivariate AR-models by threshold accepting

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  • Winker, Peter

Abstract

In econometric modelling the choice of relevant variables is of crucial importance for the Interpretation of the results. In many cases it is based on some a priori knowledge from economic theory and a rather heuristic procedure for determining other influential variables sometimes based on an Information criterion. This paper deals with an automatic method for the identification of relevant variables based solely on an Information criterion. As an example, the identification of multivariate lag structures in AR-models is studied. This issue arises e.g. for large-scale econometric models, for Granger causality tests or the application of Johansen's test for cointegration. The procedure suggested in this paper allows the optimization of the lag structure over the whole set of possible multivariate lag structures with regard to a given information criterion, e.g. the Hannan-Quinn estimator or Akaike's final prediction error criterion. The optimization is performed by the heuristic multiple purpose optimization algorithm Threshold Accepting which proved to be very successful for discrete optimization problems in economics and econometrics. The implementation of Threshold Accepting for subset identification in multivariate AR-models and some Simulation results for a bivariate model are presented.

Suggested Citation

  • 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".
    • Handle: RePEc:zbw:kondp2:224
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    Cited by:

    1. Chipman, John Somerset & Winker, Peter, 1994. "Optimal industrial classification with heteroskedasticity correction: An application to the Swedish industrial classification system," Discussion Papers, Series II 237, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    2. Iftekhar, M. S. & Tisdell, J. G., 2018. "Learning in repeated multiple unit combinatorial auctions: An experimental study," Working Papers 267301, University of Western Australia, School of Agricultural and Resource Economics.
    3. 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.
    4. Ferraresi Tommaso & Roventini Andrea & Semmler Willi, 2019. "Macroeconomic Regimes, Technological Shocks and Employment Dynamics," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(4), pages 599-625, August.
    5. 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".
    6. 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.
    7. 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.
    8. John S.nChipman & Peter Winker, "undated". "Optimal Industrial Classification in a Dynamic Model of Price Adjustment," Computing in Economics and Finance 1996 _013, Society for Computational Economics.
    9. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    10. Ivan Savin & Peter Winker, 2012. "Lasso-type and Heuristic Strategies in Model Selection and Forecasting," Jena Economics Research Papers 2012-055, Friedrich-Schiller-University Jena.
    11. 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.
    12. Manfred GILLI & Peter WINKER, 2008. "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
    13. Winker, Peter & Fang, Kai-Tai, 1995. "Application of threshold accepting to the evaluation of the discrepancy of a set of points," Discussion Papers, Series II 248, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    14. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    15. Alessandro Bellocchi & Edgar J. Sanchez Carrera & Giuseppe Travaglini, 2021. "What drives TFP long-run dynamics in five large European economies?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(2), pages 569-595, July.
    16. Andreas Sachs & Frauke Schleer, 2013. "Labour Market Performance in OECD Countries: A Comprehensive Empirical Modelling Approach of Institutional Interdependencies. WWWforEurope Working Paper No. 7," WIFO Studies, WIFO, number 46851.
    17. Sachs, Andreas & Schleer, Frauke, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," ZEW Discussion Papers 13-040, ZEW - Leibniz Centre for European Economic Research.
    18. Gilli, Manfred & Winker, Peter, 2007. "2nd Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 2-3, September.

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