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No-Regret Forecasting with Egalitarian Committees

Jiun-Hua Su

arXiv 28 Sep 2021 · Econometrics

arXiv:2109.13801 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The forecast combination puzzle is often found in literature: The equal-weight scheme tends to outperform sophisticated methods of combining individual forecasts. Exploiting this finding, we propose a hedge egalitarian committees algorithm (HECA), which can be implemented via mixed integer quadratic programming. Specifically, egalitarian committees are formed by the ridge regression with shrinkage toward equal weights; subsequently, the forecasts provided by these committees are averaged by the hedge algorithm. We establish the no-regret property of HECA. Using data collected from the ECB Survey of Professional Forecasters, we find the superiority of HECA relative to the equal-weight scheme during the COVID-19 recession.

Citation extraction

53
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100
in-text mentions
53
distinct cited
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self-citations
7,079
main-text words

appendix boundary found by appendix_command · 58% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Diebold, F. X. and M. Shin (2019) Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian LASSO and its Derivatives1.000123100%
2Conflitti, C., C. De Mol, and D. Giannone (2015) Optimal Combination of Survey Forecasts1.00053100%
3Freund, Y. and R. E. Schapire (1997) A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting0.9098475%
4Elliott, G. and A. Timmermann (2016) Economic Forecasting0.87482100%
5Genre, V., G. Kenny, A. Meyler, and A. Timmermann (2013) Combining Expert Forecasts: Can Anything Beat the Simple Average?0.87452100%
6Cesa-Bianchi, N. and F. Orabona (2021) Online Learning Algorithms0.73732100%
7Cesa-Bianchi, N. and G. Lugosi (2006) Prediction, Learning, and Games0.64441100%
8Manski, C. F (2013) Public Policy in an Uncertain World0.64422100%
9Timmermann, A (2006) Forecast Combinations, Elsevier, vol. 1 of0.64422100%
10Bates, J. M. and C. W. J. Granger (1969) The Combination of Forecasts0.51121100%

Showing the top 10 of 53 scored citations.