Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen

In recent years some paers have been bublished that deal with the forecasting performance of indicators for the German economy. The real-time aspect, however, was largely neglected. This article analyses the information content of some ifo indicators (the business climate index for the manufacturing sector and its components, the current business situation and business expectations) to predict the German index of production. The analysis is based on cross correlations, Granger causality tests and different out-of-sample forecasts, generated by ubset VAR models. First, the out-of-sample forecasts are made, as in conventional studies, with the latest available data and fixed model structure. Afterwards, the out-of-sample indicator properties are analysed in real-time, i.e. with real-time data and variable model structure. In general the indicator properties become worse under real-time conditions. The indicator-based VAR models are not able to beat the forecast performance of a pur autoregressive model for forecast horizons of one and three month. But for forecast horizons of six, nine and twelve months, the indicators seem to be useful in predicting the index of production.