Benign Overfitting in Economic Forecasting via Noise Regularization

[2312.05593] Benign Overfitting in Economic Forecasting via Noise Regularization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2312.05593 (econ) [Submitted on 9 Dec 2023 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Benign Overfitting in Economic Forecasting via Noise Regularization Authors: Yuan Liao , Xinjie Ma , Andreas Neuhierl , Zhentao Shi View a PDF of the paper titled Benign Overfitting in Economic Forecasting via Noise Regularization, by Yuan Liao and 3 other authors View PDF HTML (experimental) Abstract: This paper studies linear overparameterized models in economic forecasting and highlights that including noise variables (regressors with no predictive power) regularizes the estimator. We consider a setting where both the outcome variable and the high-dimensional predictors are driven by a small number of latent factors, and show that the linear forecast model is dense rather than sparse. It turns out that a ridgeless regression augmented with noise predictors attains the same asymptotic forecast accuracy as an oracle with known true factors, without estimating the factors or assuming them to be strong. The gain comes from shrinkage of the eigenvalues of the design matrix, which reduces the out-of-sample variance. In contrast, perfect variable selection that removes noise variables can worsen forecasts when the number of retained predictors is comparable to the sample size. Empirically, we apply this approach to forecasting U.S. inflation, international GDP growth, and the U.S. equity risk premium, finding that noise regularization improves and stabilizes predictive performance. Subjects: Econometrics (econ.EM) ; Methodology (stat.ME) Cite as: arXiv:2312.05593 [econ.EM] (or arXiv:2312.05593v3 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2312.05593 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuan Liao [ view email ] [v1] Sat, 9 Dec 2023 15:17:19 UTC (505 KB) [v2] Tue, 12 Dec 2023 02:48:05 UTC (471 KB) [v3] Tue, 14 Apr 2026 18:05:28 UTC (995 KB) Full-text links: Access Paper: View a PDF of the paper titled Benign Overfitting in Economic Forecasting via Noise Regularization, by Yuan Liao and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2023-12 Change to browse by: econ stat stat.ME References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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