Model Selection Criteria for Factor‐Augmented Regressions
27 Pages Posted: 23 Dec 2012
Date Written: February 2013
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.
JEL Classification: C22, C52, E37
Suggested Citation: Suggested Citation