Iacopini, Matteo, Poon, Aubrey, Rossini, Luca, Zhu, Dan (2023) Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP. Journal of Economic Dynamics and Control, 157 . Article Number 104757. ISSN 0165-1889. E-ISSN 1879-1743. (doi:10.1016/j.jedc.2023.104757) (KAR id:103148)
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Official URL: https://doi.org/10.1016/j.jedc.2023.104757 |
Abstract
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-atRisk, the Expected Shortfall and distance among percentiles of real GDP nowcasts.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.jedc.2023.104757 |
Uncontrolled keywords: | Bayesian inference; mixed-frequency; multivariate quantile regression; nowcasting; VAR |
Subjects: | H Social Sciences |
Divisions: | Divisions > Division of Human and Social Sciences > School of Economics |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Aubrey Poon |
Date Deposited: | 06 Oct 2023 14:47 UTC |
Last Modified: | 08 Jan 2024 18:37 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/103148 (The current URI for this page, for reference purposes) |
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