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Model Selection Criteria for Factor-Augmented Regressions


  • 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.


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.