Bayesian inference on structural impulse response functions

• 成果类型: Article
• 署名作者: Plagborg-Moller, Mikkel
• 署名单位: Princeton University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE926
• 发表日期: 2019
• 页码: 145-184
• 关键词: Bayesian inference; Hamiltonian Monte Carlo; impulse response function; news shock; nonfundamental; noninvertible; partial identification; structural vector autoregression; structural vector moving average; Whittle likelihood

摘要

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.