论文标题
贝叶斯混合频率量化矢量自动估计：引起每月美国GDP的尾巴风险
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP
论文作者
论文摘要
经济和金融体系中风险的及时表征在经济政策和私营部门的决策中都起着至关重要的作用。但是，低频变量的信息内容和条件平均模型的结果仅提供了有限的证据来研究此问题。我们提出了一个新型的混合频率分位数自动进程（MF-QVAR）模型来解决此问题。受单变量贝叶斯分位数回归文献的启发，在贝叶斯框架下利用了多元不对称的拉普拉斯分布，以形成可能性。与精确采样器相结合的数据增强方法有效地估计了状态空间表示下较高频率下缺少的低频变量。所提出的方法使我们能够以高频的多个感兴趣变量的有条件分位数，并在高频下得出与分位数相关的风险度量，从而实现及时的策略干预措施。该模型的主要应用是现实地播放了美国GDP的条件分位数，这与价值风险和预期的不足的量化严格相关。
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 nowcast 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 nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk and the Expected Shortfall.
