no code implementations • 20 May 2024 • Fabrizio Iacone, Luca Rossini, Andrea Viselli
We consider forecast comparison in the presence of instability when this affects only a short period of time.
no code implementations • 7 Feb 2024 • Andrea Bastianin, Elisabetta Mirto, Yan Qin, Luca Rossini
We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market.
no code implementations • 10 Aug 2023 • Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
Then, the proposed framework is exploited to examine the distributional effects of money growth on the distributions of inflation and its disaggregate measures in the United States and the Euro area.
no code implementations • 29 Nov 2022 • Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of US macro and financial indicators, where the homoskedasticity assumption is relaxed to allow for time-varying volatility.
no code implementations • 5 Sep 2022 • Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions.
no code implementations • 9 Nov 2020 • Niko Hauzenberger, Michael Pfarrhofer, Luca Rossini
In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances.
no code implementations • 24 Jul 2020 • Claudia Foroni, Francesco Ravazzolo, Luca Rossini
Recent research finds that forecasting electricity prices is very relevant.
no code implementations • 29 Jun 2020 • Florian Huber, Luca Rossini
The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher.
1 code implementation • 6 Sep 2019 • Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini, Weixuan Zhu
In this paper we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalised logistic regression model.
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