Model Selection Criteria for Factor-Augmented Regressions - Federal Reserve Bank of New York

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Staff Reports

Model Selection Criteria for Factor-Augmented Regressions

February 2009 Number 363
Revised: May 2012

JEL classification: C22, C52, E37

Authors: Jan J. J. Groen and George Kapetanios

Factor-augmented regressions are often used as a parsimonious way of modeling a variable using information from a large data set, through a few factors estimated from this data set. But how does one determine which factors are relevant for such a regression? Existing work has focused on criteria that can consistently estimate the appropriate number of factors in a large-dimensional panel of explanatory variables. However, these are not necessarily all relevant for modeling a specific dependent variable within a factor-augmented regression. This paper develops a number of theoretical conditions selection criteria have to fulfill in order to provide a consistent estimate of the factor dimension that is relevant for such a regression. Our framework takes into account factor estimation error, and it does not depend on a specific factor estimation methodology. Our conditions indicate that standard model selection criteria, such as BIC, are not consistent for factor-augmented regressions, but they can be once we modify these such that the corresponding penalty function for dimensionality also penalizes factor estimation error. We show through Monte Carlo and empirical applications that these modified information criteria are useful in determining appropriate factor-augmented regressions.

Available only in PDF pdf

29 pages / 470 kb

For a published version of this report, see Jan J. J. Groen and George Kapetanios, "Model Selection Criteria for Factor-Augmented Regressions," Oxford Bulletin of Economics and Statistics 75, no. 1 (February 2013): 37-63.

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