A Canonical Correlation Approach for Selecting the Number of Dynamic Factors

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Abstract

In this article, we propose a selection procedure that allows us to consistently estimate the number of dynamic factors in a dynamic factor model. The procedure is based on a canonical correlation analysis of the static factors which has the advantage of being invariant to a rescaling of the factors. Monte Carlo simulations suggest that the proposed selection rule outperforms existing ones, in particular, if the contribution of the common factors to the overall variance is moderate or low. The new selection procedure is applied to the US macroeconomic data panel used in Stock and Watson [NBER working paper 11467 (2005)].

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