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 / Forni, Mario; Giovannelli, Alessandro; Lippi, Marco; Soccorsi, Stefano. - In: JOURNAL OF APPLIED ECONOMETRICS. - ISSN 0883-7252. - 33:5(2018), pp. 625-642.
Data di pubblicazione: | 2018 |
Titolo: | Dynamic factor model with infinite-dimensional factor space: Forecasting |
Autore/i: | Forni, Mario; Giovannelli, Alessandro; Lippi, Marco; Soccorsi, Stefano |
Autore/i UNIMORE: | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1002/jae.2634 |
Rivista: | |
Volume: | 33 |
Fascicolo: | 5 |
Pagina iniziale: | 625 |
Pagina finale: | 642 |
Codice identificativo ISI: | WOS:000440550900001 |
Codice identificativo Scopus: | 2-s2.0-85050970147 |
Citazione: | Dynamic factor model with infinite-dimensional factor space: Forecasting / Forni, Mario; Giovannelli, Alessandro; Lippi, Marco; Soccorsi, Stefano. - In: JOURNAL OF APPLIED ECONOMETRICS. - ISSN 0883-7252. - 33:5(2018), pp. 625-642. |
Tipologia | Articolo su rivista |
File in questo prodotto:

I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris