Triple the Gamma — A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models

Cadonna, Annalisa and Frühwirth-Schnatter, Sylvia and Knaus, Peter ORCID: https://orcid.org/0000-0001-6498-7084 (2020) Triple the Gamma — A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models. Econometrics, 8 (20). ISSN 2225-1146

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Abstract

Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of explanatory variables on the outcome variable. However, in particular when the number of explanatory variables is large, there is a known risk of overfitting and poor predictive performance, since the effect of some explanatory variables is constant over time. We propose a new 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 dataset and for a series of macroeconomics variables in the Euro Area.

Item Type: Article
Keywords: Bayesian model averaging; horseshoe prior; lasso prior; sparsity; stochastic volatility; triple gamma prior; VAR models
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 08 Jan 2021 13:00
Last Modified: 08 Jan 2021 13:00
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/98328/
URI: https://epub.wu.ac.at/id/eprint/7930

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