VAR model training using particle swarm optimisation: evidence from macro-finance data
by George Filis, Kyriakos Kentzoglanakis, Christos Floros
International Journal of Computational Economics and Econometrics (IJCEE), Vol. 1, No. 1, 2009

Abstract: This paper examines the empirical relationship between CPI, oil prices, stock market and unemployment in EU15 using a new computational approach. In particular, we propose a novel approach to train the well-known vector autoregressive (VAR) model using a particle swarm optimisation (PSO) method. Results demonstrate that PSO succeeds in training the model parameters. Furthermore, as the prediction error is found to be low, this strengthens the validity and usability of PSO as a model training method. The empirical results suggest that oil is an important determinant of CPI and stock market changes. Oil price changes affect CPI positively and stock market negatively. Finally, we report no evidence that CPI and unemployment have a negative effect on stock market performance.

Online publication date: Fri, 06-Nov-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Economics and Econometrics (IJCEE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com