Finding Starting-Values for Maximum Likelihood Estimation of Vector STAR Models

38 Pages Posted: 10 Oct 2013

See all articles by Frauke Schleer

Frauke Schleer

ZEW – Leibniz Centre for European Economic Research

Date Written: October 7, 2013

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.

Keywords: Vector STAR model, starting-values, optimization heuristics, grid search, estimation, non-linearieties

JEL Classification: C32, C61, C63

Suggested Citation

Schleer, Frauke, Finding Starting-Values for Maximum Likelihood Estimation of Vector STAR Models (October 7, 2013). ZEW - Centre for European Economic Research Discussion Paper No. 13-076, Available at SSRN: https://ssrn.com/abstract=2337997 or http://dx.doi.org/10.2139/ssrn.2337997

Frauke Schleer (Contact Author)

ZEW – Leibniz Centre for European Economic Research ( email )

P.O. Box 10 34 43
L 7,1
D-68034 Mannheim, 68034
Germany

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