Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods

Niko Hauzenberger, Florian Huber and Gary Koop
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Niko Hauzenberger: University of Salzburg

No 2305, Working Papers from University of Strathclyde Business School, Department of Economics

Abstract: Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which re ects the empirical regularity that TVPs are typically sparse (i.e., time variation may occur only episodically and only for some of the coecients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using arti cial data we demonstrate the accuracy and computational eciency of our methods. In an application involving the term structure of interest rates in the eurozone, we nd our dynamic shrinkage prior to e ectively pick out small amounts of parameter change and our methods to forecast well

Keywords: Time-varying parameter regression; dynamic shrinkage prior; global-local shrinkage prior; Bayesian variable selection; scalable Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C30 C50 E3 E43 (search for similar items in EconPapers)
Pages: pages
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Related works:
Journal Article: Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods (2024)
Working Paper: Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods (2023)
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