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

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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.

Suggested Citation

  • Kirstin Hubrich & Frauke Skudelny, 2016. "Forecast Combination for Euro Area Inflation - A Cure in Times of Crisis?," Finance and Economics Discussion Series 2016-104, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2016-104
    DOI: 10.17016/FEDS.2016.104
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    1. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
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    3. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
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    16. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    17. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    18. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
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    Cited by:

    1. Zivile Zekaite & Gabe de Bondt & Elke Hahn, 2017. "Alice: A New Inflation Monitoring Tool," EcoMod2017 10414, EcoMod.
    2. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
    3. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    4. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    5. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    6. Patricia Toledo & Roberto Duncan, 2024. "Forecasting food price inflation during global crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1087-1113, July.
    7. Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.
    8. Chad Fulton & Kirstin Hubrich, 2021. "Forecasting US Inflation in Real Time," Econometrics, MDPI, vol. 9(4), pages 1-20, October.
    9. Marcus P. A. Cobb, 2020. "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, vol. 58(1), pages 287-312, January.
    10. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    11. Gerdesmeier Dieter & Roffia Barbara & Reimers Hans-Eggert, 2017. "Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 19-34, December.

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    More about this item

    Keywords

    Forecasting; Euro area inflation; forecast combinations; Forecast evaluation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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