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Finding Starting-Values for the Estimation of Vector STAR Models

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  • Frauke Schleer

    (Centre for European Economic Research (ZEW), P.O. Box 103443, Mannheim D-68034, Germany)

Abstract

This paper focuses on finding starting-values for the estimation of Vector STAR models. Based on a Monte Carlo study, different procedures are evaluated. Their performance is assessed with respect to model fit and computational effort. I employ (i) grid search algorithms and (ii) heuristic optimization procedures, namely differential evolution, threshold accepting, and simulated annealing. In the equation-by-equation starting-value search approach the procedures achieve equally good results. Unless the errors are cross-correlated, equation-by-equation search followed by a derivative-based algorithm can handle such an optimization problem sufficiently well. This result holds also for higher-dimensional Vector STAR models with a slight edge for heuristic methods. For more complex Vector STAR models which require a multivariate search approach, simulated annealing and differential evolution outperform threshold accepting and the grid search.

Suggested Citation

  • Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, vol. 3(1), pages 1-26, January.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:1:p:65-90:d:45287
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    5. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    6. Kadilli, Anjeza & Krishnakumar, Jaya, 2022. "Smooth Transition Simultaneous Equation Models," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    7. Murat Midiliç, 2020. "Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 87-117, January.

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