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Recursive Forecast Combination For Dependent Heterogeneous Data

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  • Sancetta, Alessio

Abstract

This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and only require a tail condition on the data sequences. The theoretical results show that the bounds are also valid in the case of time varying combination weights.

Suggested Citation

  • Sancetta, Alessio, 2010. "Recursive Forecast Combination For Dependent Heterogeneous Data," Econometric Theory, Cambridge University Press, vol. 26(02), pages 598-631, April.
  • Handle: RePEc:cup:etheor:v:26:y:2010:i:02:p:598-631_10
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    Cited by:

    1. Kajal Lahiri & Huaming Peng & Xuguang Sheng, 2015. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," CESifo Working Paper Series 5468, CESifo Group Munich.
    2. 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.
    3. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Sep 2016.
    4. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    5. Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-005, The George Washington University, Department of Economics, Research Program on Forecasting.
    6. 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.

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