Macroeconomic Forecasting and Machine Learning

Ta-Chung Chi, Ting-Han Fan, Raffaele Ghigliazza, Domenico Giannone and Wang, Zixuan (Kevin)

No 20727, CEPR Discussion Papers from Centre for Economic Policy Research

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: Regularization (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 C55 (search for similar items in EconPapers)
Date: 2025-10
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Working Paper: Macroeconomic Forecasting and Machine Learning (2025)
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