Abstract
AbstractThis 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.
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