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Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting

Mario Forni (), Alessandro Giovannelli (), Marco Lippi () and Stefano Soccorsi ()

No ECARES 2016-16, Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: Abstract. The paper compares the pseudo real-time forecasting performance of threeDynamic 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 employa 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 outperfomed by (i) and (ii) over thefull sample for Industrial Production.

New Economics Papers: this item is included in nep-ets, nep-for and nep-mac
Date: 2016-03
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Related works:
Journal Article: Dynamic factor model with infinite‐dimensional factor space: Forecasting (2018) Downloads
Working Paper: Dynamic Factor model with infinite dimensional factor space: forecasting (2016) Downloads
Working Paper: Dynamic Factor model with infinite dimensional factor space: forecasting (2016) Downloads
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