Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Swiss National Bank Working Papers,
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay.
Are Qualitative Inflation Expectations Useful to Predict Inflation?
Journal of Business Cycle Measurement and Analysis,
This paper examines the properties of qualitative inflation expectations collected from economic experts for Germany. It describes their characteristics relating to rationality and Granger causality. An out-of-sample simulation study investigates whether this indicator is suitable for inflation forecasting. Results from other standard forecasting models are considered and compared with models employing survey measures. We find that a model using survey expectations outperforms most of the competing models. Moreover, we find some evidence that the survey indicator already contains information from other model types (e. g. Phillips curve models). However, the forecast quality may be further improved by completely taking into account information from some financial indicators.