Taming data-driven probability distributions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00599855" target="_blank" >RIV/67985556:_____/25:00599855 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11230/25:10514022
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/for.3208" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/for.3208</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/for.3208" target="_blank" >10.1002/for.3208</a>
Alternative languages
Result language
angličtina
Original language name
Taming data-driven probability distributions
Original language description
We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. By allowing complex time series patterns to be learned from a data-rich environment, our approach is useful for decision making that depends on the uncertainty of a large number of economic outcomes. In particular, it is informative for agents facing asymmetric dependence of their loss on the outcomes of possibly non-Gaussian and non-linear variables. We demonstrate the usefulness of the proposed approach on two different datasets where a machine learns patterns from the data. First, we illustrate the gains in predicting stock return distributions that are heavy tailed and asymmetric. Second, we construct macroeconomic fan charts that reflect information from a high-dimensional dataset.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
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
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Forecasting
ISSN
0277-6693
e-ISSN
1099-131X
Volume of the periodical
44
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
30
Pages from-to
676-691
UT code for WoS article
001357994900001
EID of the result in the Scopus database
2-s2.0-85209813913