Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
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. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
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.
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Are Qualitative Inflation Expectations Useful to Predict Inflation?
Rolf Scheufele
Journal of Business Cycle Measurement and Analysis,
Nr. 1,
2011
Abstract
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.
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Should We Trust in Leading Indicators? Evidence from the Recent Recession
Katja Drechsel, Rolf Scheufele
Abstract
The paper analyzes leading indicators for GDP and industrial production in Germany. We focus on the performance of single and pooled leading indicators during the pre-crisis and crisis period using various weighting schemes. Pairwise and joint significant tests are used to evaluate single indicator as well as forecast combination methods. In addition, we use an end-of-sample instability test to investigate the stability of forecasting models during the recent financial crisis. We find in general that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis the relative performance of many leading indicator models increased. At short horizons, survey indicators perform best, while at longer horizons financial indicators, such as term spreads and risk spreads, improve relative to the benchmark.
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