Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/89471 
Year of Publication: 
2003
Series/Report no.: 
LEM Working Paper Series No. 2003/13
Publisher: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Abstract: 
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.
Subjects: 
dynamic factor models
principal components
time series
large cross-sections
panel data
forecasting
JEL: 
C13
C33
C43
Document Type: 
Working Paper

Files in This Item:
File
Size
626.04 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.