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Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Forecast Combination for Euro Area Inflation - A Cure in Times of Crisis?
Kirstin Hubrich
Frauke Skudelny
Abstract

The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short-term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance-based forecast combination methods, in particular one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time-varying weights assign weights to the economic interpretations of the forecast stemming from different models. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre-crisis times, the period after the global financial crisis and the full evaluation period including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that, first, forecast combination helps hedge against bad forecast performance and, second, that performance-based weighting tends to outperform simple averaging.


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Kirstin Hubrich & Frauke Skudelny, Forecast Combination for Euro Area Inflation - A Cure in Times of Crisis?, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2016-104, Aug 2016.
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Keywords: Forecasting ; Euro area inflation ; Forecast combinations ; Forecast evaluation
DOI: 10.17016/FEDS.2016.104
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