Finding starting-values for the estimation of vector STAR models

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 1 ; Pages: 65-90 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Optimization Techniques; Programming Models; Dynamic Analysis ; Computational Techniques; Simulation Modeling
• Thema: Vector STAR model ; starting-values ; optimization heuristics ; grid search ; estimation ; non-linearieties

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Schleer, Frauke
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2015

Weitere Informationen

• DOI: doi:10.3390/econometrics3010065
• Handle: http://hdl.handle.net/10419/171817
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ