Bayesian inference on structural impulse response functions

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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 distribution 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.

Original languageEnglish (US)
Pages (from-to)145-184
Number of pages40
JournalQuantitative Economics
Volume10
Issue number1
DOIs
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Bayesian inference
  • C11
  • C32
  • Hamiltonian Monte Carlo
  • Whittle likelihood
  • impulse response function
  • news shock
  • nonfundamental
  • noninvertible
  • partial identification
  • structural vector autoregression
  • structural vector moving average

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