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Forecasting with Many Models: Model Confidence Sets and Forecast Combination

Author

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  • Jon D. Samuels
  • Rodrigo Sekkel

Abstract

A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging. We compare different trimming schemes and propose a new one based on Model Confidence Sets that take into account the statistical significance of historical out-of-sample forecasting performance. In an empirical application of forecasting U.S. macroeconomic indicators, we find significant gains in out-of-sample forecast accuracy from our proposed trimming method.

Suggested Citation

  • Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Staff Working Papers 13-11, Bank of Canada.
  • Handle: RePEc:bca:bocawp:13-11
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    References listed on IDEAS

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    5. Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajšsek, 2013. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," The Review of Economics and Statistics, MIT Press, pages 1501-1519.
    6. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
    7. Peter Tulip, 2009. "Has the Economy Become More Predictable? Changes in Greenbook Forecast Accuracy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1217-1231, September.
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    Citations

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    Cited by:

    1. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    2. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, pages 199-212.
    4. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    5. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, Open Access Journal, vol. 3(4), pages 1-28, November.

    More about this item

    Keywords

    Econometric and statistical methods;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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