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subject:"Schätzung"
type_genre:"Übersichtsarbeit"
~isPartOf:"CREATES research paper"
~subject:"Welt"
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A neural network approach to the environmental Kuznets curve
Bennedsen, Mikkel
;
Hillebrand, Eric
;
Jensen, Sebastian
-
2022
Persistent link: https://www.econbiz.de/10013367388
Saved in:
2
The New Keynesian model and bond yields
Andreasen, Martin Møller
-
2021
Persistent link: https://www.econbiz.de/10012433979
Saved in:
3
Now- and backcasting initial claims with high-dimensional daily internet search-volume data
Borup, Daniel
;
Rapach, David E.
;
Montes Schütte, Erik …
-
2021
Persistent link: https://www.econbiz.de/10012433998
Saved in:
4
The incremental information in the yield curve about future interest rate risk
Christensen, Bent Jesper
;
Kjær, Mads Markvart
; …
-
2021
-
This version: June 28, 2021
Persistent link: https://www.econbiz.de/10012621334
Saved in:
5
Fractional integration and cointegration
Haulde, Javier
;
Nielsen, Morten Ørregaard
-
2021
Persistent link: https://www.econbiz.de/10012816374
Saved in:
6
Temperature anomalies, long memory, and aggregation
Vera-Valdés, J. Eduardo
-
2020
Persistent link: https://www.econbiz.de/10012433973
Saved in:
7
Roughness in spot variance? : a GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures
Bolko, Anine E.
;
Christensen, Kim
;
Pakkanen, Mikko S.
; …
-
2020
Persistent link: https://www.econbiz.de/10012318238
Saved in:
8
Bond risk premiums at the zero lower bound
Andreasen, Martin Møller
;
Jørgensen, Kasper
;
Meldrum, …
-
2019
Persistent link: https://www.econbiz.de/10012063987
Saved in:
9
Explaining bond return predictability in an estimated New Keynesian model
Andreasen, Martin Møller
-
2019
Persistent link: https://www.econbiz.de/10012063989
Saved in:
10
Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model
He, Changli
;
Kang, Jian
;
Teräsvirta, Timo
;
Zhang, Shuhua
-
2019
Persistent link: https://www.econbiz.de/10012316892
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