Dynamic Factor model with infinite dimensional factor space: forecasting

Mario Forni, Alessandro Giovannelli, Marco Lippi and Stefano Soccorsi

No 11161, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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. (2015b) and Forni et al. (2015a). 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 nd 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 the full sample for Industrial Production.

Date: 2016-03
New Economics Papers: this item is included in nep-ets and nep-for
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Related works:
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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