Double Descent and Benign Overfitting in Macroeconomic Forecasting

2026-05-14

AI Summary

This study investigates the origins and conditions under which double descent and benign overfitting arise in macroeconomic forecasting. To address the challenge that medium-dimensional macroeconomic datasets often fail to satisfy the overparameterization requirements for benign overfitting, the authors propose a factor-model-based synthetic data augmentation strategy that reformulates the forecasting problem as kernel ridge regression with a factor-structured kernel. Theoretical analysis elucidates the mechanism through which benign overfitting remains effective in such settings. Empirical results demonstrate that the proposed approach significantly outperforms the classical Stock–Watson dynamic factor model on both the FRED-MD and FRED-QD datasets, with predictive gains increasing as the forecast horizon extends.

Abstract

We study double descent and benign overfitting in macroeconomic forecasting. We document that double-descent risk curves arise in standard macroeconomic datasets that are driven by a small number of latent factors, and we characterize when the underlying benign-overfitting mechanism holds. The conditions of Bartlett et al. (2020) are satisfied under the exact factor model and can also hold under the more realistic approximate factor model, provided idiosyncratic variances are not too dispersed across series. Because macroeconomic panels have only moderate dimensions, the overparameterization ratio N/T required by the theory is not naturally available. Our solution is to augment the data with synthetic copies from an estimated factor model and we prove that this strategy converges to a kernel ridge regression with a factor-structured kernel. Using monthly (FRED-MD) and quarterly (FRED-QD) US data, the resulting estimator consistently outperforms the Stock-Watson factor model for point forecasting across all series and horizons, with gains that are pervasive, statistically significant, and increasing with the forecast horizon. Our results suggest that benign overfitting, when it works, succeeds because overparameterization implicitly constructs a well-behaved kernel, not because overparameterization is intrinsically desirable.

Problem

• double descent
• benign overfitting
• macroeconomic forecasting
• factor model
• overparameterization

Innovation

• benign overfitting
• double descent
• factor model
• kernel ridge regression
• data augmentation