| 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. |
