I propose a Bayesian approach to identify vector autoregressive (VAR) models via proxies in a data-rich environment. The setup augments a small-scale VAR model with latent factors. It allows to trace out the responses of disaggregated series in a unified model while controlling for broad economic conditions. The posterior sampler accounts for the estimation uncertainty in these latent factors as well as the measurement precision of the proxy. In a first application to monetary policy, I extract factors from a wide range of real and financial series and find that the effects of monetary policy shocks vary along the yield curve. In a second application to oil market shocks I add disaggregated US series to a standard model of the global oil market. I find that negative news about future oil supply have adverse effects on the US economy.

我提出了一种贝叶斯方法，以在数据丰富的环境中通过代理识别向量自回归（VAR）模型。该设置使用潜在因素扩充了小型VAR模型。它允许在控制广泛的经济条件的同时，以统一的模型追踪分类序列的响应。后采样器考虑了这些潜在因素中的估计不确定性以及代理的测量精度。在对货币政策的首次应用中，我从大量实际和金融系列中提取了因素，并发现货币政策冲击的影响沿收益率曲线变化。在石油市场冲击的第二个应用中，我将分类的美国系列添加到全球石油市场的标准模型中。我发现有关未来石油供应的负面消息对美国经济产生不利影响。