Aggregate shocks and macroeconomic fluctuations: A bayesian approach
介绍贝叶斯技术分析总体冲击对宏观经济时间序列的影响，通过计算响应概率密度函数并利用蒙特卡洛或吉布斯采样评估其性质，发现不确定性度量远大于传统方法。
Abstract This paper describes Bayesian techniques for analysing the effects of aggregate shocks on macroeconomic time‐series. Rather than calculate point estimates of the response of a time‐series to an aggregate shock, we calculate the whole probability density function of the response and use Monte‐Carlo or Gibbs sampling techniques to evaluate its properties. The proposed techniques impose identification restrictions in a way that includes the uncertainty in these restrictions, and thus are an improvement over traditional approaches that typically use least‐squares techniques supplemented by bootstrapping. We apply these techniques in the context of two different models. A key finding is that measures of uncertainty, such as posterior standard deviations, are much larger than are their classical counterparts.