Bayesian inference on structural impulse response functions

By Mikkel Plagborg-Møller. Quantitative Economics, 2019

63 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a7e003b26e167336cdbaf4f4401a94d9ddf8fa7

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In Computer Vision and Pattern Recognition

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Citations in the corpus

• Identifying Shocks via Time-Varying Volatility (2019, background): “Stock & Watson (2017), Plagborg-Møller (2018)). In short, if there are more than n underlying shocks in the economy, the true H is non-invertible. However, it is almost always necessary to assume H is invertible for identification purposes (the recent work of Chahrour & Jurado (2017) discusses some ”
• Identification in Macroeconomics (2017, background): “See Hansen and Sargent (1991), Fernandez-Villaverde et al. (2007), and Plagborg-Møller (2017).”