The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model, Stock and Watson (2002a), (ii) The model based on generalized principal components, Forni et al. (2005), (iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). 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. Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms

Forni, M., A., Giovannelli, M., Lippi e S., Soccorsi. "Dynamic Factor model with infinite dimensional factor space: forecasting" Working paper, RECENT WORKING PAPER SERIES, Dipartimento di Economia Marco Biagi – Università di Modena e Reggio Emilia, 2016.

Dynamic Factor model with infinite dimensional factor space: forecasting

Forni, M.;
2016-01-01

Abstract

The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model, Stock and Watson (2002a), (ii) The model based on generalized principal components, Forni et al. (2005), (iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). 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. Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms
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Forni, M.; Giovannelli, A.; Lippi, M.; Soccorsi, S.
Forni, M., A., Giovannelli, M., Lippi e S., Soccorsi. "Dynamic Factor model with infinite dimensional factor space: forecasting" Working paper, RECENT WORKING PAPER SERIES, Dipartimento di Economia Marco Biagi – Università di Modena e Reggio Emilia, 2016.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1292915
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