We thank two anonymous referees as well as the Editor, Anindya Banerjee, for helpful comments, and Craig Kennedy for excellent research assistance. The views expressed in this article are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System.
Model Selection Criteria for Factor-Augmented Regressions*
Article first published online: 7 SEP 2012
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2012
Oxford Bulletin of Economics and Statistics
Special Issue: Large Data Sets
Volume 75, Issue 1, pages 37–63, February 2013
How to Cite
Groen, J. J. J. and Kapetanios, G. (2013), Model Selection Criteria for Factor-Augmented Regressions. Oxford Bulletin of Economics and Statistics, 75: 37–63. doi: 10.1111/j.1468-0084.2012.00721.x
- Issue published online: 21 DEC 2012
- Article first published online: 7 SEP 2012
- Final Manuscript Received:
Existing dynamic factor selection criteria determine the appropriate number of factors in a large-dimensional panel of explanatory variables, but not all of these have to be relevant for modeling a specific dependent variable within a factor-augmented regression. We develop theoretical conditions that selection criteria have to meet in order to get consistent estimates of the relevant factor dimension for such a regression. These incorporate factor estimation error and do not depend on specific factor estimation methodologies. Using this framework, we modify standard model selection criteria, and simulation and empirical applications indicate that these are useful in determining appropriate factor-augmented regressions.