Bayesian Inference on Structural Impulse Response Functions

Mikkel Plagborg-Moller

Working Paper from Harvard University OpenScholar

Abstract: I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid simulation of the posterior of the impulse responses is possible using an algorithm that exploits the Whittle likelihood. The impulse responses are partially identified, and I derive the frequentist asymptotics of the Bayesian procedure to show which features of the prior information are updated by the data. The procedure is used to estimate the effects of technological news shocks on the U.S. business cycle.

Date: 2015-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (7)

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http://scholar.harvard.edu/plagborg/node/344351

Related works:
Journal Article: Bayesian inference on structural impulse response functions (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:qsh:wpaper:344351

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