Niet beschikbaar in het Nederlands
Riccardo Cristadoro
- 3 June 2008
- WORKING PAPER SERIES - No. 900Forecasting inflation and tracking monetary policy in the euro area: does national information help?Details
- Abstract
- The ECB objective is set in terms of year on year growth rate of the Euro area HICP. Nonetheless, a good deal of attention is given to national data by market analysts when they try to anticipate monetary policy moves. In this paper we use the Generalized Dynamic Factor model to develop a set of core inflation indicators that, combining national data with area wide information, allow us to answer two related questions. The first is whether country specific data actually bear any relevance for the future path of area wide price growth, over and above that already contained in area wide data. The second is whether in order to track ECB monetary policy decisions it is useful to take into account national information and not only area wide statistics. In both cases our findings point to the conclusion that, once area wide information is properly taken into account, there is little to be gained from considering national idiosyncratic developments.
- JEL Code
- C25 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Discrete Regression and Qualitative Choice Models, Discrete Regressors, Proportions
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 8 May 2008
- OCCASIONAL PAPER SERIES - No. 84Details
- Abstract
- This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best.
- JEL Code
- E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods