0581659 20250207140318.220240119235959.9 85183570255 001170311200001 10.1016/j.frl.2024.105003 7 s. P cav_un_epca*03619971544-612361Elsevier Fan charts Probabilistic forecasting Machine learning cav_un_auth*0242028 Baruník Jozef UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0462008 Hanus Luboš UTIA-B Ekonometrie Department of Econometrics E E CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://www.sciencedirect.com/science/article/pii/S1544612324000333?dgcid=author http://library.utia.cas.cz/separaty/2023/E/barunik-0581659.pdf GX19-28231X GA ČR CZ cav_un_auth*0385135 We propose how to construct big data-driven macroeconomic fan charts, using machine learning methods to reflect the information in 216 relevant economic variables. Such data-rich fan charts do not rely on restrictive model assumptions and allow the exploration of non-Gaussian, asymmetric, heavy-tailed data and their non-linear interactions. By allowing complex patterns to be learned from a data-rich environment, our fan charts are useful for decision making that depends on the uncertainty of a potentially large number of economic variables — most public policy issues. WOS AH 50000 50200 50202 2025 2 RVO:67985556 https://hdl.handle.net/11104/0349774 S 105003 C BUSINESS.FINANCE 7.2 1.2 2.5 0.02839 1.076 28265 123 1.711 26.94 8.12 22462 Q1 5.800 1615 Q1 95.2 2.13 Q1 95.2 2024 85183570255 SCOPUS PUBMED 001170311200001 WOS cav_un_epca*0361997 Finance Research Letters 1544-6123 1544-6131 Roč. 61 č. 1 2024 Elsevier PRINT