TY - JOUR
T1 - Bayesian mixed-frequency quantile vector autoregression
T2 - eliciting tail risks of monthly US GDP
AU - Iacopini, Matteo
AU - Poon, Aubrey
AU - Rossini, Luca
AU - Zhu, Dan
N1 - Funding Information:
The authors gratefully acknowledge two anonymous referees and the editor, Juan Rubio Ramirez. The authors thank Roberto Casarin, Todd Clark, Florian Huber, Mark Jensen, Simone Manganelli, Massimiliano Marcellino, Michael Pfarrhofer, Francesco Ravazzolo and seminar participants at ESOBE 2022 for their useful feedback. Luca Rossini acknowledges financial support from the Italian Ministry of University and Research ( MUR ) under the Department of Excellence 2023-2027 grant agreement “Centre of Excellence in Economics and Data Science” (CEEDS).
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/12
Y1 - 2023/12
N2 - Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to analyse conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to detect the vulnerability in the US economy and then to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk, the Expected Shortfall and distance among percentiles of real GDP nowcasts.
AB - Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to analyse conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to detect the vulnerability in the US economy and then to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk, the Expected Shortfall and distance among percentiles of real GDP nowcasts.
KW - Bayesian inference
KW - Mixed-frequency
KW - Multivariate quantile regression
KW - Nowcasting
KW - VAR
UR - http://www.scopus.com/inward/record.url?scp=85173873445&partnerID=8YFLogxK
U2 - 10.1016/j.jedc.2023.104757
DO - 10.1016/j.jedc.2023.104757
M3 - Article
AN - SCOPUS:85173873445
SN - 0165-1889
VL - 157
JO - Journal of Economic Dynamics and Control
JF - Journal of Economic Dynamics and Control
M1 - 104757
ER -