Arbeitspapier
Finding starting-values for maximum likelihood estimation of vector STAR models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: ZEW Discussion Papers ; No. 13-076
- 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
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Schleer, Frauke
- Ereignis
-
Veröffentlichung
- (wer)
-
Zentrum für Europäische Wirtschaftsforschung (ZEW)
- (wo)
-
Mannheim
- (wann)
-
2013
- Handle
- URN
-
urn:nbn:de:bsz:180-madoc-347917
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Schleer, Frauke
- Zentrum für Europäische Wirtschaftsforschung (ZEW)
Entstanden
- 2013