A forecasting performance comparison of dynamic factor models based on static and dynamic methods

We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson in [1], [2]. The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin in [3], [4]. The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni in [5], [6]. The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in-sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to [7], but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.