Determining the Structure of Dynamic Factor Models

We propose two procedures for determining the number of dynamic factors, extending Bai and Ng (2002) and Ahn and Horenstein (2013) to dynamic factor models where lagged factors may directly influence the observed variables.

As an intermediate step, we develop a simple and computationally efficient alternating least squares algorithm that directly estimates the dynamic factors, rather than their static representations.

By working with these direct estimates, our approach enables joint determination of the number of factors and the filter length.

Our test is shown to be consistent under weaker conditions than those in Bai and Ng (2007) and Amengual and Watson (2007).

We apply our procedures to estimate the number of primitive shocks in a large panel of US macroeconomic time series.