Yuan Liao, Xinjie Ma, Andreas Neuhierl, Zhentao Shi
arXiv 9 Dec 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2312.05593 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses a key question in economic forecasting: does pure noise truly lack predictive power? Economists typically conduct variable selection to eliminate noises from predictors. Yet, we prove a compelling result that in most economic forecasts, the inclusion of noises in predictions yields greater benefits than its exclusion. Furthermore, if the total number of predictors is not sufficiently large, intentionally adding more noises yields superior forecast performance, outperforming benchmark predictors relying on dimension reduction. The intuition lies in economic predictive signals being densely distributed among regression coefficients, maintaining modest forecast bias while diversifying away overall variance, even when a significant proportion of predictors constitute pure noises. One of our empirical demonstrations shows that intentionally adding 300 6,000 pure noises to the Welch and Goyal (2008) dataset achieves a noteworthy 10% out-of-sample R square accuracy in forecasting the annual U.S. equity premium. The performance surpasses the majority of sophisticated machine learning models.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 1.000 | 5 | 3 | 100% |
| 2 | Welch, Ivo and Goyal, Amit (2008) A comprehensive look at the empirical performance of equity premium prediction | 0.843 | 4 | 3 | 75% |
| 3 | Giannone, Domenico and Lenza, Michele and Primiceri, Giorgio E (2021) Economic predictions with big data: The illusion of sparsity | 0.811 | 4 | 2 | 100% |
| 4 | Hastie, Trevor and Montanari, Andrea and Rosset, Saharon and Tibshir… (2022) Surprises in high-dimensional ridgeless least squares interpolation | 0.811 | 4 | 2 | 100% |
| 5 | Barro, Robert J and Lee, Jong-Wha (1994) Sources of economic growth | 0.737 | 3 | 2 | 100% |
| 6 | Belkin, Mikhail and Hsu, Daniel and Ma, Siyuan and Mandal, Soumik (2019) Reconciling modern machine-learning practice and the classical bias–variance trade-off | 0.737 | 3 | 2 | 100% |
| 7 | McCracken, Michael W and Ng, Serena (2016) FRED-MD: A monthly database for macroeconomic research | 0.737 | 3 | 2 | 100% |
| 8 | Lee, Sungyoon and Lee, Sokbae (2023) The Mean Squared Error of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors | 0.644 | 2 | 2 | 100% |
| 9 | Mei, Song and Montanari, Andrea (2019) The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve | 0.644 | 2 | 2 | 100% |
| 10 | Meng, Xuran and Cao, Yuan and Wang, Weichen (2025) Estimation of out-of-sample sharpe ratio for high dimensional portfolio optimization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors | 0.405 | 1 | 1 |