Sylvia Frühwirth-Schnatter, Peter Knaus
arXiv 25 Jul 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2207.12147 · PDF · DOI · OpenAlex · Extracted main text
In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using a non-centered parametrization. We discuss efficient MCMC estimation and provide an application to US inflation modeling.
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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 | A. Cadonna, 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 | 1.000 | 14 | 4 | 100% |
| 2 | S. Frühwirth-Schnatter and H. Wagner (2010) Stochastic model specification search for Gaussian and partially non-Gaussian state space models | 1.000 | 12 | 3 | 100% |
| 3 | A. Bitto and S. Frühwirth-Schnatter (2019) Achieving shrinkage in a time-varying parameter model framework | 1.000 | 10 | 4 | 100% |
| 4 | M. Belmonte, G. Koop, and D. Korobolis (2014) Hierarchical shrinkage in time-varying parameter models | 0.928 | 4 | 3 | 100% |
| 5 | C.M. Carvalho, N.G. Polson, and J.G. Scott (2009) Handling sparsity via the horseshoe | 0.737 | 3 | 2 | 100% |
| 6 | A.C. Harvey (1989) Forecasting, Structural Time Series Models and the Kalman Filter | 0.737 | 3 | 2 | 100% |
| 7 | A. Bhadra, J. Datta, N.G. Polson, and B. Willard (2019) Lasso meets horseshoe: A survey | 0.644 | 2 | 2 | 100% |
| 8 | C.M. Carvalho, N.G. Polson, and J.G. Scott (2010) The horseshoe estimator for sparse signals | 0.644 | 2 | 2 | 100% |
| 9 | C.K. Carter and R. Kohn (1994) On Gibbs sampling for state space models | 0.644 | 2 | 2 | 100% |
| 10 | S. Frühwirth-Schnatter (2004) Computationally efficient Bayesian parameter estimation for state space models based on reparameterizations | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! | 0.405 | 1 | 1 |