Forecast Combination for Euro Area Inflation: A Cure in Times of Crisis?

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We use models that provide forecasts of the five main components of euro area HICP on a monthly basis for the period 1990(1) to 2014(6) and most are updated at least on a quarterly basis. We analyse the results for the full forecast period, but also for the pre-crisis and the postcrisis period and present the evaluation of the aggregation of the component forecasts. We carry out our forecast combination and evaluation based on every third month, as in the quarterly projections of the Eurosystem. We aggregate the disaggregate forecasts to headline HICP inflation forecasts by using the weights that would last have been available and known to the forecaster in real time. Terms of use: Documents in EconStor mayThree different combination approaches are included in our forecast model comparison: the simple average, using equal weights; a performance-based forecast combination using the root mean squared error for a rolling 2-year window of the most recent past to weight the different forecasts; and a performance-based forecast combination as above, with geometrically (backwards) decaying weights, i.e. recent performance is given more weight than performance longer ago.We find that the best model for forecasting differs depending on whether the overall HICP or the HICP excluding food and energy is considered, and which period and forecast horizon is studied. Therefore we conclude that performance-based forecast combination helps to hedge against bad forecast performance of some of the models in some situations, even though in the presence of large shocks or crises it does not necessarily improve over the best forecast model since the forecast accuracy of all models might, for example, be biased in the same way.Performance-based forecast combination appears to be useful when the models included in the set of models exhibit very different forecast performance over time. ECB Working Paper 1972, October 2016 2Forecast combination for the full sample period typically improves forecast accuracy over the autoregressive benchmark model for core and headline inflation, and often improves over single multivariate models. The forecast accuracy gain of combinations is largest for inflation excluding energy and food for the full sample. We also find that performance-based forecast combination improves significantly over the simple average for our application.Investigating combination weights and their development over time, we find significant changes in the weights. Moreov...

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