dynamic factor model with infinite‐dimensional factor space: forecasting (replication data)
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 US 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 model (iii) significantly outperforms models (i) and (ii) in the Great Moderation period for both industrial production and inflation, and that model (iii) is also the best method for inflation over the full sample. However, model (iii) is outperformed by models (ii) and (i) over the full sample for industrial production.
Dynamic factor model with infinite‐dimensional factor space: Forecasting (replication data).
Journal of Applied Econometrics.
Forni, M., Giovannelli, A., Lippi, M. and Soccorsi, S. (2018), Dynamic Factor Model With Infinite‐Dimensional Factor Space: Forecasting, Journal of Applied Econometrics, 33(5), 625-642. https://doi.org/10.1002/jae.2634