Ta-Chung Chi, Ting-Han Fan, Raffaele M. Ghigliazza, Domenico Giannone, Zixuan, Wang
arXiv 13 Oct 2025 · Econometrics
arXiv:2510.11008 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Giannone, D., M. Lenza, and G. E. Primiceri (2021) Economic predictions with big data: The illusion of sparsity self | 1.000 | 8 | 4 | 100% |
| 2 | De Mol, C., D. Giannone, and L. Reichlin (2008) Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? | 1.000 | 6 | 3 | 100% |
| 3 | Banbura, M., D. Giannone, and L. Reichlin (2010, None) (2010) Large bayesian vector auto regressions | 1.000 | 5 | 4 | 100% |
| 4 | Giannone, D., M. Lenza, and G. E. Primiceri (2015) Prior selection for vector autoregressions self | 1.000 | 5 | 4 | 100% |
| 5 | De Mol, C. D., E. Gautier, D. Giannone, S. Mullainathan, L. Reichlin… (2017) Big Data in Economics: Evolution or Revolution?, pp.\ 612–632 | 0.928 | 4 | 3 | 100% |
| 6 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 0.843 | 3 | 3 | 100% |
| 7 | Carriero, A., D. Pettenuzzo, and S. Shekhar (2024) Macroeconomic Forecasting with Large Language Models | 0.843 | 3 | 3 | 100% |
| 8 | Giacomini, R. and I. Komunjer (2005) Evaluation and combination of conditional quantile forecasts | 0.811 | 4 | 2 | 100% |
| 9 | Komunjer, I (2013) Quantile Prediction | 0.737 | 3 | 2 | 100% |
| 10 | D’Agostino, A. and D. Giannone (2012) Comparing Alternative Predictors Based on Large‐Panel Factor Models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 89 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Double Descent and Benign Overfitting in Macroeconomic Forecasting | 0.405 | 1 | 1 |