The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting
Mario Forni (),
Marc Hallin (),
Marco Lippi () and
Lucrezia Reichlin ()
No 3432, CEPR Discussion Papers from C.E.P.R. Discussion Papers
This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic factor model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
Keywords: dynamic factor models; forecasting; large cross-sections; panel data; principal components; time series (search for similar items in EconPapers)
JEL-codes: C13 C33 C43 (search for similar items in EconPapers)
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Journal Article: The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting (2005)
Working Paper: The generalised dynamic factor model: one sided estimation and forecasting (2005)
Working Paper: The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting (2003)
Working Paper: The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting (2003)
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