The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting

This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic factor model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain 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 consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.