Working Paper: CEPR ID: DP3146
Authors: Mario Forni; Marc Hallin; Marco Lippi; Lucrezia Reichlin
Abstract: The Paper uses a large data set, consisting of 447 monthly macroeconomic time series concerning the main countries of the Euro area to simulate out-of-sample predictions of the Euro area industrial production and the harmonized inflation index and to evaluate the role of financial variables in forecasting. We considered two models which allow forecasting based on large panels of time series: Forni, Hallin, Lippi, and Reichlin (2000, 2001c) and Stock and Watson (1999). Performance of both models was compared to that of a simple univariate AR model. Results show that multivariate methods outperform univariate methods for forecasting inflation at one, three, six, and twelve months and industrial production at one and three months. We find that financial variables do help forecasting inflation, but do not help forecasting industrial production.
Keywords: business cycle; dynamic factor models; financial variables; forecasting; principal components
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 |
|---|---|
| Financial variables (G19) | Inflation forecasting (E31) |
| Financial variables (G19) | Industrial production forecasting (E27) |