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Working Paper
The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure
This paper studies the effects of monetary policy shocks using structural VARs. We achieve identification by imposing sign and zero restrictions on the systematic component of monetary policy. Importantly, our identification scheme does not restrict the contemporaneous response of output to a monetary policy shock. Using data for the period 1965?2007, we consistently find that an increase in the federal funds rate induces a contraction in output. We also find that monetary policy shocks are contractionary during the Great Moderation. Finally, we show that the identification strategy in Uhlig ...
Working Paper
The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure
Following Leeper, Sims, and Zha (1996), we identify monetary policy shocks in SVARs by restricting the systematic component of monetary policy. In particular, we impose sign and zero restrictions only on the monetary policy equation. Since we do not restrict the response of output to a monetary policy shock, we are agnostic in Uhlig's (2005) sense. But, in contrast to Uhlig (2005), our results support the conventional view that a monetary policy shock leads to a decline in output. Hence, our results show that the contractionary effects of monetary policy shocks do not hinge on questionable ...
Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy-SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often ...
Working Paper
Narrative Sign Restrictions for SVARs
We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and/or the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions tend to be highly informative. Even a single narrative sign restriction may dramatically sharpen and even change the inference of SVARs originally identified via traditional sign restrictions. Our approach combines ...
Working Paper
Uniform Priors for Impulse Responses
There has been a call for caution when using the conventional method for Bayesian inference in set-identified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for individual impulse responses or other quantity of interest. This paper challenges this call by formally showing that, when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of ...
Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often ...
Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify structural shocks. We illustrate our methods with two applications. In particular, we show how to generalize the ...