Working Paper: CEPR ID: DP3432
Authors: Mario Forni; Marc Hallin; Marco Lippi; Lucrezia Reichlin
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.
Keywords: dynamic factor models; forecasting; large cross-sections; panel data; principal components; time series
JEL Codes: C13; C33; C43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| two-step forecasting method (C53) | consistent estimation of the common component (C51) |
| estimation of covariance matrices (C10) | construction of aggregates that minimize idiosyncratic variance (C43) |
| construction of aggregates that minimize idiosyncratic variance (C43) | consistent estimator for the common component (C51) |
| consistent estimator for the common component (C51) | forecasts that converge to the optimal forecast (C53) |
| two-step forecasting method (C53) | forecasts that outperform traditional models (C53) |
| proposed method (C59) | enhancement of reliability of forecasts (C53) |