Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus
arXiv 6 Dec 2019 · Econometrics · publishedEconometrics (2020) · 8 citations (OpenAlex)
arXiv:1912.03100 · PDF · DOI · OpenAlex · Extracted main text
Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of a predictor on the outcome variable. However, in particular when the number of predictors is large, there is a known risk of overfitting and poor predictive performance, since the effect of some predictors is constant over time. We propose a prior for variance shrinkage in TVP models, called triple gamma. The triple gamma prior encompasses a number of priors that have been suggested previously, such as the Bayesian lasso, the double gamma prior and the Horseshoe prior. We present the desirable properties of such a prior and its relationship to Bayesian Model Averaging for variance selection. The features of the triple gamma prior are then illustrated in the context of time varying parameter vector autoregressive models, both for simulated datasets and for a series of macroeconomics variables in the Euro Area.
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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 | Bitto, A.; Frühwirth-Schnatter, S (2019) Achieving Shrinkage in a Time-Varying Parameter Model Framework | 1.000 | 14 | 4 | 100% |
| 2 | Carvalho, C.M.; Polson, N.G.; Scott, J.G (2009) Handling sparsity via the horseshoe | 1.000 | 5 | 4 | 100% |
| 3 | Belmonte, M.A.G.; Koop, G.; Korobolis, D (2014) Hierarchical shrinkage in time-varying parameter models | 0.928 | 4 | 3 | 100% |
| 4 | Frühwirth-Schnatter, S.; Wagner, H (2010) Stochastic Model Specification Search for Gaussian and partially Non-Gaussian State Space Models | 0.928 | 4 | 3 | 100% |
| 5 | Griffin, J.E.; Brown, P.J (2017) Hierarchical Shrinkage Priors for Regression Models | 0.928 | 4 | 3 | 100% |
| 6 | Carvalho, C.M.; Polson, N.G.; Scott, J.G (2010) The horseshoe estimator for sparse signals | 0.843 | 3 | 3 | 100% |
| 7 | Armagan, A.; Dunson, D.B.; Clyde, M (2011) Generalized beta mixtures of Gaussians | 0.737 | 3 | 2 | 100% |
| 8 | Carriero, A.; Clark, T.G.; Marcellino, M (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.737 | 3 | 2 | 100% |
| 9 | Bhadra, A.; Datta, J.; Polson, N.G.; Willard, B Lasso meets horsheshoe: A survey | 0.644 | 2 | 2 | 100% |
| 10 | Chan, J.C.; Eisenstat, E (2016) Bayesian model comparison for time-varying parameter VARs with stochastic volatilty | 0.644 | 2 | 2 | 100% |
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