We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the non-seasonally adjusted data...

In this paper, the authors comment on the Monte Carlo results of the paper by Lucchetti and Veneti (A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics), 2020)) that studies and compares the performance of the...

Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman...