Dynamic Factor model with infinite dimensional factor space: forecasting
Mario Forni (),
Alessandro Giovannelli (),
Marco Lippi () and
Stefano Soccorsi ()
Center for Economic Research (RECent) from University of Modena and Reggio E., Dept. of Economics "Marco Biagi"
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 (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, and for Inflation over the full sample. However, (iii) is outperformed by (i) and (ii) over the full sample for Industrial Production.
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Journal Article: Dynamic factor model with infinite‐dimensional factor space: Forecasting (2018)
Working Paper: Dynamic Factor model with infinite dimensional factor space: forecasting (2016)
Working Paper: Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:mod:recent:120
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