Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods

Hauzenberger Niko (), Florian Huber and Gary Koop
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Hauzenberger Niko: Department of Economics, University of Strathclyde, Glasgow, UK

Studies in Nonlinear Dynamics & Econometrics, 2024, vol. 28, issue 2, 201-225

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 reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively 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)
Date: 2024
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Related works:
Working Paper: Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods (2023)
Working Paper: Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
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DOI: 10.1515/snde-2022-0077

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