Finding Starting-Values for the Estimation of Vector STAR Models
AbstractThis 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
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Schleer, F. Finding Starting-Values for the Estimation of Vector STAR Models. Econometrics 2015, 3, 65-90.
Schleer F. Finding Starting-Values for the Estimation of Vector STAR Models. Econometrics. 2015; 3(1):65-90.Chicago/Turabian Style
Schleer, Frauke. 2015. "Finding Starting-Values for the Estimation of Vector STAR Models." Econometrics 3, no. 1: 65-90.