Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171817
Authors: 
Schleer, Frauke
Year of Publication: 
2015
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 3 [Year:] 2015 [Issue:] 1 [Pages:] 65-90
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 derivativ-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.
Subjects: 
Vector STAR model
starting-values
optimization heuristics
grid search
estimation
non-linearieties
JEL: 
C32
C61
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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