Boosting diffusion indices
Article first published online: 16 MAR 2009
Copyright © 2009 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 24, Issue 4, pages 607–629, June/July 2009
How to Cite
Bai, J. and Ng, S. (2009), Boosting diffusion indices. J. Appl. Econ., 24: 607–629. doi: 10.1002/jae.1063
- Issue published online: 27 APR 2009
- Article first published online: 16 MAR 2009
- NSF. Grant Numbers: SES-0 551 275, SES-0 549 978
In forecasting and regression analysis, it is often necessary to select predictors from a large feasible set. When the predictors have no natural ordering, an exhaustive evaluation of all possible combinations of the predictors can be computationally costly. This paper considers ‘boosting’ as a methodology of selecting the predictors in factor-augmented autoregressions. As some of the predictors are being estimated, we propose a stopping rule for boosting to prevent the model from being overfitted with estimated predictors. We also consider two ways of handling lags of variables: a componentwise approach and a block-wise approach. The best forecasting method will necessarily depend on the data-generating process. Simulations show that for each data type there is one form of boosting that performs quite well. When applied to four key economic variables, some form of boosting is found to outperform the standard factor-augmented forecasts and is far superior to an autoregressive forecast. Copyright © 2009 John Wiley & Sons, Ltd.