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Econometrics 2015, 3(1), 65-90; doi:10.3390/econometrics3010065

Finding Starting-Values for the Estimation of Vector STAR Models

Centre for European Economic Research (ZEW), P.O. Box 103443, Mannheim D-68034, Germany
Received: 26 October 2014 / Revised: 26 November 2014 / Accepted: 4 January 2015 / Published: 29 January 2015
(This article belongs to the Special Issue Non-Linear Regression Modeling)
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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. View Full-Text
Keywords: Vector STAR model; starting-values; optimization heuristics; grid search; estimation; non-linearieties Vector STAR model; starting-values; optimization heuristics; grid search; estimation; non-linearieties
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Schleer, F. Finding Starting-Values for the Estimation of Vector STAR Models. Econometrics 2015, 3, 65-90.

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