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Bayesian Inference on Structural Impulse Response Functions

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  • Mikkel Plagborg-Møller

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.

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  • Mikkel Plagborg-Møller, 2015. "Bayesian Inference on Structural Impulse Response Functions," Working Paper 344351, Harvard University OpenScholar.
  • Handle: RePEc:qsh:wpaper:344351
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    File URL: http://scholar.harvard.edu/plagborg/node/344351
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    Cited by:

    1. Silvia Miranda-Agrippino & Giovanni Ricco, 2015. "The Transmission of Monetary Policy Shocks," Discussion Papers 1711, Centre for Macroeconomics (CFM), revised Feb 2017.
    2. Fabio Canova & Filippo Ferroni, 2018. "Mind the gap! Stylized dynamic facts and structural models," Working Papers No 13/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

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