Niko Hauzenberger, Florian Huber, Gary Koop
arXiv 8 May 2020 · Econometrics · publishedStudies in Nonlinear Dynamics and Econometrics (2023) · 9 citations (OpenAlex)
arXiv:2005.03906 · PDF · DOI · OpenAlex · Extracted main text
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 works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Johndrow JE, Orenstein P, and Bhattacharya A (2017) Bayes shrinkage at GWAS scale: Convergence and approximation theory of a scalable MCMC algorithm for the horseshoe prior | 1.000 | 11 | 4 | 100% |
| 2 | Bhattacharya A, Chakraborty A, and Mallick BK (2016) Fast sampling with Gaussian scale mixture priors in high-dimensional regression | 1.000 | 6 | 3 | 100% |
| 3 | Huber F, Koop G, and Onorante L (2021) Inducing sparsity and shrinkage in time-varying parameter models | 0.928 | 4 | 3 | 100% |
| 4 | Primiceri G (2005) Time varying structural autoregressions and monetary policy | 0.909 | 8 | 3 | 75% |
| 5 | Kowal DR, Matteson DS, and Ruppert D (2019) Dynamic shrinkage processes | 0.894 | 7 | 3 | 71% |
| 6 | Ray P, and Bhattacharya A (2018) Signal Adaptive Variable Selector for the Horseshoe Prior | 0.737 | 3 | 2 | 100% |
| 7 | Hahn PR, and Carvalho CM (2015) Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective | 0.737 | 3 | 2 | 100% |
| 8 | Carvalho CM, Polson NG, and Scott JG (2010) The horseshoe estimator for sparse signals | 0.644 | 2 | 2 | 100% |
| 9 | Cogley T, Primiceri GE, and Sargent TJ (2010) Inflation-gap persistence in the US | 0.644 | 2 | 2 | 100% |
| 10 | Fischer MM, Hauzenberger N, Huber F, and Pfarrhofer M (2023) General Bayesian time-varying parameter vector autoregressions for modeling government bond yields | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0.54cm Time-Varying Parameters as Ridge Regressions | 0.405 | 1 | 1 |
| 2 | A New Perspective of the Meese-Rogoff Puzzle: Application of Sparse Dynamic Shrinkage | 0.405 | 1 | 1 |