Fan charts in era of big data and learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00581659" target="_blank" >RIV/67985556:_____/24:00581659 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/24:10482537 RIV/00216208:11230/24:10482537
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1544612324000333?dgcid=author" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1544612324000333?dgcid=author</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.frl.2024.105003" target="_blank" >10.1016/j.frl.2024.105003</a>

Result language
angličtina
Original language name
Fan charts in era of big data and learning
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
50202 - Applied Economics, Econometrics

Project
<a href="/en/project/GX19-28231X" target="_blank" >GX19-28231X: DyMoDiF - Dynamic Models for the Digital Finance</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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