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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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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

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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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