Fan charts in era of big data and learning

Jozef Baruník and Luboš Hanus

Finance Research Letters, 2024, vol. 61, issue C

Abstract: 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.

Keywords: Fan charts; Probabilistic forecasting; Machine learning; Deep learning; Macroeconomic time series (search for similar items in EconPapers)
JEL-codes: C45 C53 E17 E37 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:61:y:2024:i:c:s1544612324000333

DOI: 10.1016/j.frl.2024.105003

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