Macroeconomic Forecasting and Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2510.11008,
title = {Macroeconomic Forecasting and Machine Learning},
author = {Ta-Chung Chi and Ting-Han Fan and Raffaele M. Ghigliazza and Domenico Giannone and Zixuan and Wang},
journal= {arXiv preprint arXiv:2510.11008},
year = {2025}
}