Backward-in-Time Selection of the Order of Dynamic Regression Prediction Model
Article first published online: 26 JUL 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 32, Issue 8, pages 685–701, December 2013
How to Cite
Vlachos, I. and Kugiumtzis, D. (2013), Backward-in-Time Selection of the Order of Dynamic Regression Prediction Model. J. Forecast., 32: 685–701. doi: 10.1002/for.2265
- Issue published online: 21 NOV 2013
- Article first published online: 26 JUL 2013
- Manuscript Accepted: 10 JAN 2013
- Manuscript Revised: 27 NOV 2012
- Manuscript Received: 4 MAY 2012
- time series;
- dynamic regression;
- variable selection
We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward-in-Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.