Finding starting-values for maximum likelihood estimation of vector STAR models


Schleer, Frauke


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URL: https://ub-madoc.bib.uni-mannheim.de/34791
URN: urn:nbn:de:bsz:180-madoc-347917
Document Type: Working paper
Year of publication: 2013
The title of a journal, publication series: ZEW Discussion Papers
Volume: 13-076
Place of publication: Mannheim
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 330 Economics
Classification: JEL: C32 , C61 , C63,
Keywords (English): Vector STAR model , starting-values , optimization heuristics , grid search , estimation , non-linearieties
Abstract: This paper focuses on finding starting-values for maximum likelihood estimation of Vector STAR models. Based on a Monte Carlo exercise, different procedures are evaluated. Their performance is assessed w.r.t. 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 VSTAR models with a slight edge for the heuristic methods. Being faced with more complex Vector STAR models for which a multivariate search approach is required, simulated annealing and differential evolution outperform threshold accepting and the grid with a zoom.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




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Schleer, Frauke (2013) Finding starting-values for maximum likelihood estimation of vector STAR models. Open Access ZEW Discussion Papers Mannheim 13-076 [Working paper]
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