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The Accuracy of Short-Term Forecast Combinations

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Abstract

This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.

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  • Eleonora Granziera & Corinne Luu & Pierre St-Amant, 2013. "The Accuracy of Short-Term Forecast Combinations," Bank of Canada Review, Bank of Canada, vol. 2013(Summer), pages 13-21.
    • Handle: RePEc:bca:bcarev:v:2013:y:2013:i:summer13:p:13-21
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    Cited by:

    1. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    2. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    3. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    4. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    5. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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