# Macroeconomic Forecasting and Machine Learning

arXiv: https://arxiv.org/abs/2510.11008 · HTML: https://mlsanity.com/paper/2510.11008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 81 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [A scale-dependent notion of effective dimension](https://mlsanity.com/paper/2001.10872/) (2020)
- [Macroeconomic Forecasting with Large Language Models](https://mlsanity.com/paper/2407.00890/) (2024)
- [Bayesian Neural Networks for Macroeconomic Analysis](https://mlsanity.com/paper/2211.04752/) (2022)

## Built on by (most cited first, 1 of 1)

- [Online Continual Learning for Time Series: a Natural Score-driven Approach](https://mlsanity.com/paper/2601.12931/) (2026)

## Abstract (arXiv)

We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regularization via shrinkage is essential to control model complexity, while introducing nonlinearities yields limited improvements in predictive accuracy. Out-of-sample validation plays a critical role in selecting model architecture and preventing overfitting.