To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP
Queen Mary University of London, United Kingdom.
Örebro University, Örebro University School of Business. University of Kent, United Kingdom.ORCID iD: 0000-0003-2587-8779
University of Milan, Italy; Fondazione Eni Enrico Mattei, Italy.
Monash University, Australia.ORCID iD: 0000-0003-1487-2232
2023 (English)In: Journal of Economic Dynamics and Control, ISSN 0165-1889, E-ISSN 1879-1743, Vol. 157, article id 104757Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 157, article id 104757
Keywords [en]
Bayesian inference, Mixed-frequency, Multivariate quantile regression, Nowcasting, VAR
National Category
Economics
Identifiers
URN: urn:nbn:se:oru:diva-109019DOI: 10.1016/j.jedc.2023.104757ISI: 001105923400001Scopus ID: 2-s2.0-85173873445OAI: oai:DiVA.org:oru-109019DiVA, id: diva2:1805461
Note

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).

Available from: 2023-10-17 Created: 2023-10-17 Last updated: 2023-12-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Poon, Aubrey

Search in DiVA

By author/editor
Poon, AubreyZhu, Dan
By organisation
Örebro University School of Business
In the same journal
Journal of Economic Dynamics and Control
Economics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf