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Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 10 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 145-184
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian inference
Hamiltonian Monte Carlo
impulse response function
news shock
nonfundamental
noninvertible
partial identification
structural vector autoregression
structural vector moving average
Whittle likelihood
JEL: 
C11
C32
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