Flexible Mixture Priors for Large Time-varying Parameter Models

Hauzenberger, Niko

doi:10.1016/j.ecosta.2021.06.001

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…A standard approach used to select the state dynamics of the TVPs is based on estimating different models separately and then using model selection criteria to discriminate between competing specifications (see, for example, Sims and Zha, 2006;Koop et al, 2009;Hauzenberger, 2021). This approach has the limitation that only a single distinct model 1 Reaching the zero lower bound in response to economic slack is one example of a structural break.…”

Section: Introductionmentioning

confidence: 99%

General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields

Fischer

Hauzenberger

Huber

et al. 2022

J of Applied Econometrics

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US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility which treats the nature of parameter dynamics as unknown.Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out model selection, we adopt Bayesian shrinkage priors. Our framework is applied to model the US yield curve. We show that the model forecasts well, and focus on selected in-sample features to analyze determinants of structural breaks in US yield curve dynamics.

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Section: Introductionmentioning

confidence: 99%

General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields

Fischer

Hauzenberger

Huber

et al. 2022

J of Applied Econometrics

Self Cite

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“…In this case, the exact MCMC algorithm of Bhattacharya, Chakraborty, and Mallick (2016) is the state of the art. 1 Kastner and Huber (2020) , Hauzenberger (2021) and Korobilis (2022) , for example, use this exact algorithm in the context of large VARs to reduce the computational burden of estimating these models. However, even it is too computationally slow to handle the huge number of regressors that appear in the static representation of the TVP regression model.…”

Section: Introductionmentioning

confidence: 99%

“… 1 Kastner and Huber (2020) , Hauzenberger (2021) and Korobilis (2022) , for example, use this exact algorithm in the context of large VARs to reduce the computational burden of estimating these models. …”

Section: Introductionmentioning

confidence: 99%

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

Hauzenberger,

Huber,

Koop

2023

Studies in Nonlinear Dynamics &Amp; Econometrics

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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.

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“…The literature has dealt with the issue of selecting the appropriate law of motion for timevarying parameters (TVPs) by estimating different models separately and then using model selection criteria to discriminate between competing specifications (see, e.g., Sims and Zha, 2006;Koop et al, 2009;Hauzenberger, 2020). But, to the best of our knowledge, no attempt has been made to develop models that rely on a large set of competing laws of motion for the coefficients and decide which one describes the data best.…”

Section: Introductionmentioning

confidence: 99%

General Bayesian time-varying parameter VARs for predicting government bond yields

Fischer,

Hauzenberger,

Huber

et al. 2021

Preprint

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Time-varying parameter (TVP) regressions commonly assume that timevariation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In this paper, we propose a flexible TVP VAR that assumes the TVPs to depend on a panel of partially latent covariates. The latent part of these covariates differ in their state dynamics and thus capture smoothly evolving or abruptly changing coefficients. To determine which of these covariates are important, and thus to decide on the appropriate state evolution, we introduce Bayesian shrinkage priors to perform model selection. As an empirical application, we forecast the US term structure of interest rates and show that our approach performs well relative to a set of competing models. We then show how the model can be used to explain structural breaks in coefficients related to the US yield curve.

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Flexible Mixture Priors for Large Time-varying Parameter Models

Cited by 10 publications

References 60 publications

General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields

General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields

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

General Bayesian time-varying parameter VARs for predicting government bond yields

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