Working Paper: CEPR ID: DP3893
Authors: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
Abstract: In this Paper we evaluate the role of a set of variables as leading indicators for Euro-area inflation and GDP growth. Our evaluation is based on using the variables in the ECB euro area model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator with that of purely autoregressive models, using an evaluation procedure that is particularly relevant for policy-making. The evaluation is conducted both ex-post and in a pseudo real time context, for several forecast horizons, and using both recursive and rolling estimation. We also analyse three different approaches to combining the information from several indicators. First, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Second, an automated model selection procedure is applied to models with a large set of indicators. Third, we consider pooling the single indicator forecasts. The results indicate that single indicator forecasts are on average better than those derived from more complicated methods, but for them to beat the autoregression a different indicator has to be used in each period. A simple real-time procedure for indicator-selection produces good results.
Keywords: factor model; GDP growth; inflation; leading indicator; model selection
JEL Codes: C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| leading indicators (E32) | forecasting accuracy (C53) |
| single indicator forecasts (C53) | outperform autoregressive models (C22) |
| labor market variables, commodity prices, and fiscal indicators (E24) | outperform autoregressive models (C22) |
| US indicators (C80) | beneficial for forecasting euro area metrics (F37) |
| economic shocks (F69) | influence relevance of specific indicators (C43) |