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~person:"Bonato, Matteo"
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El Niño and La Niña
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agricultural commodities
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forecasting
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random forests
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realized variance
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Bonato, Matteo
Pierdzioch, Christian
18
Gupta, Rangan
13
Hufe, Paul
7
POEL, D. VAN DEN
7
Behrens, Christoph
6
Sadorsky, Perry A.
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Brunori, Paolo
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Krauss, Christopher
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Kugler, Philipp
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Medeiros, Marcelo C.
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Mahler, Daniel Gerszon
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Do, Xuan Anh
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Foltas, Alexander
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Gillas, Konstantinos Gkillas
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Hamori, Shigeyuki
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Hapfelmeier, A.
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Hothorn, Torsten
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Kinkyō, Takuji
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Masini, Ricardo P.
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Montes Schütte, Erik Christian
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Ulm, K.
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Zeileis, Achim
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Alqararah, Khatab
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Baier, Daniel
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Bouri, Elie
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Chen, Jau-er
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Del Fava, Santino
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El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices : evidence from a machine learning approach
Bonato, Matteo
;
Çepni, Oğuzhan
;
Gupta, Rangan
; …
- In:
Journal of forecasting
42
(
2023
)
4
,
pp. 785-801
Persistent link: https://www.econbiz.de/10014292795
Saved in:
2
El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach
Bonato, Matteo
;
Çepni, Oğuzhan
;
Gupta, Rangan
; …
- In:
Journal of Forecasting
42
(
2022
)
4
,
pp. 785-801
means of
random
forests
to capture in a purely data‐driven way potentially nonlinear links between El Niño and La Niña and …
Persistent link: https://www.econbiz.de/10014503817
Saved in:
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