Quotation Cadonna, Annalisa, Frühwirth-Schnatter, Sylvia, Knaus, Peter. 2020. Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models. Econometrics. 8 (2), 20 ff.


RIS


BibTeX

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.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Econometrics
Language English
Title Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models
Volume 8
Number 2
Year 2020
Page from 20 ff.
URL https://www.mdpi.com/2225-1146/8/2/20/pdf
DOI http://dx.doi.org/10.3390/econometrics8020020
Open Access Y
Open Access Link https://www.mdpi.com/2225-1146/8/2/20

Associations

People
Cadonna, Annalisa (Former researcher)
Frühwirth-Schnatter, Sylvia (Details)
Knaus, Peter (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search