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Online learning and forecast combination in unbalanced panels

Author

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  • Kajal Lahiri
  • Huaming Peng
  • Yongchen Zhao

Abstract

This article evaluates the performance of a few newly proposed online forecast combination algorithms and compares them with some of the existing ones including the simple average and that of Bates and Granger (1969). We derive asymptotic results for the new algorithms that justify certain established approaches to forecast combination including trimming, clustering, weighting, and shrinkage. We also show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, so that the performance of the resulting combined forecasts are not comparable. After explicitly imputing the missing observations in the U.S. Survey of Professional Forecasters (SPF) over 1968 IV-2013 I, we find that the equally weighted average continues to be hard to beat, but the new algorithms can potentially deliver superior performance at shorter horizons, especially during periods of volatility clustering and structural breaks.

Suggested Citation

  • Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2017. "Online learning and forecast combination in unbalanced panels," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 257-288, March.
  • Handle: RePEc:taf:emetrv:v:36:y:2017:i:1-3:p:257-288
    DOI: 10.1080/07474938.2015.1114550
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    Citations

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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.
    2. Qian, Wei & Rolling, Craig A. & Cheng, Gang & Yang, Yuhong, 2022. "Combining forecasts for universally optimal performance," International Journal of Forecasting, Elsevier, vol. 38(1), pages 193-208.
    3. Meryem Duygun & Jiaqi Hao & Anders Isaksson & Robin C. Sickles, 2017. "World Productivity Growth: A Model Averaging Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 587-619, October.
    4. Ulrich Hounyo & Kajal Lahiri, 2021. "Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach," CREATES Research Papers 2021-14, Department of Economics and Business Economics, Aarhus University.
    5. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 215-228.
    6. Antonio Martin Arroyo & Aranzazu de Juan Fernandez, 2020. "Split-then-Combine simplex combination and selection of forecasters," Papers 2012.11935, arXiv.org.
    7. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    8. Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
    9. 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, H. O. Stekler Research Program on Forecasting.
    10. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.

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