Peter Knaus, Sylvia Frühwirth-Schnatter
arXiv 16 Dec 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2312.10487 · PDF · DOI · OpenAlex · Extracted main text
Many existing shrinkage approaches for time-varying parameter (TVP) models assume constant innovation variances across time points, inducing sparsity by shrinking these variances toward zero. However, this assumption falls short when states exhibit large jumps or structural changes, as often seen in empirical time series analysis. To address this, we propose the dynamic triple gamma prior -- a stochastic process that induces time-dependent shrinkage by modeling dependence among innovations while retaining a well-known triple gamma marginal distribution. This framework encompasses various special and limiting cases, including the horseshoe shrinkage prior, making it highly flexible. We derive key properties of the dynamic triple gamma that highlight its dynamic shrinkage behavior and develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. The proposed approach is evaluated through sparse covariance modeling and forecasting of the returns of the EURO STOXX 50 index, demonstrating favorable forecasting performance.
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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 | Kowal, D. R., D. S. Matteson, and D. Ruppert (2019) Dynamic shrinkage processes | 1.000 | 9 | 3 | 100% |
| 2 | Cadonna, A., S. Frühwirth-Schnatter, and P. Knaus (2020) Triple the gamma – A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models | 0.928 | 10 | 7 | 80% |
| 3 | Bitto, A. and S. Frühwirth-Schnatter (2019) Achieving shrinkage in a time-varying parameter model framework | 0.843 | 4 | 3 | 75% |
| 4 | Kalli, M. and J. E. Griffin (2014) Time-varying sparsity in dynamic regression models | 0.737 | 3 | 2 | 100% |
| 5 | Gourieroux, C. and J. Jasiak (2006) Autoregressive gamma processes | 0.644 | 3 | 2 | 67% |
| 6 | Carvalho, C. M., N. G. Polson, and J. G. Scott (2010) The horseshoe estimator for sparse signals | 0.644 | 2 | 2 | 100% |
| 7 | Frühwirth-Schnatter, S. and H. Wagner (2010) Stochastic model specification search for Gaussian and partially non-Gaussian state space models | 0.644 | 2 | 2 | 100% |
| 8 | Kastner, G. and S. Frühwirth-Schnatter (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models | 0.585 | 3 | 3 | 33% |
| 9 | Frühwirth-Schnatter, S (2006) Finite Mixture and Markov Switching Models | 0.405 | 1 | 1 | 100% |
| 10 | George, E. I. and R. McCulloch (1993) Variable selection via Gibbs sampling | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A New Perspective of the Meese-Rogoff Puzzle: Application of Sparse Dynamic Shrinkage | 0.405 | 1 | 1 |
| 2 | Flexible Bayesian Models for Time-Varying Income Distributions | 0.405 | 1 | 1 |