Finding Starting-Values for the Estimation of Vector STAR Models

Econometrics

Frauke Schleer¹ 

[1] Centre for European Economic Research (ZEW), P.O. Box 103443, Mannheim D-68034, Germany; E-Mail

关键词: Vector STAR model;    starting-values;    optimization heuristics;    grid search;    estimation;    non-linearieties;   

DOI  :  10.3390/econometrics3010065

来源: mdpi

【 摘 要 】

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 derivative-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.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.