The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model introduced by Stock and Watson in 2002, (ii) The model based on generalized principal components, introduced by Forni, Hallin, Lippi and Reichlin in 2005, (iii) The model recently proposed by Forni, Hallin, Lippi and Zaffaroni in 2015. We employ a large monthly dataset of macroeconomic and financial time series for the U.S. economy, which includes the Great Moderation, the Great Recession and the subsequent recovery (an update of the so-called Stock and Watson dataset). Using a rolling window for estimation and prediction, we find that (iii) significantly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, that (iii) is also the best method for Inflation over the full sample. However, (iii) is outperformed by (ii) and (i) over the full sample for Industrial Production.