FEEM - Fondazione ENI Enrico Mattei
FEEM - Fondazione ENI Enrico Mattei
11/21/2023 | Press release | Distributed by Public on 11/21/2023 05:10
Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP
Data
21.11.2023
Autori
Matteo Iacopini (Queen Mary University of London); Aubrey Poon (Örebro Universit and University of Kent); Luca Rossini (University of Milan and Fondazione Eni Enrico Mattei); Dan Zhu (Monash University)
Parole chiave:
Bayesian inference, Mixed-frequency, Multivariate quantile regression, Nowcasting, VAR
Publisher
Science Direct
Editor
Elsevier
JOURNAL
Journal of Economic Dynamics and Control, Volume 157, December 2023, 104757