Quotation Knaus, Peter, Frühwirth-Schnatter, Sylvia. 2021. The Dynamic Triple Gamma Prior. Joint Statistical Meetings, Seattle, United States/USA, 08.08-12.08.


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Abstract

Time-varying parameter (TVP) models are widely used in time series analysis for their ability to capture gradual changes in the effect of explanatory variables on an outcome variable of interest. The high degree of flexibility they offer can lead to overfitting when not properly regularized, which in turn results in poor out of sample predictive performance. On the other hand, approaches that are too restrictive risk not letting salient features of the data filter through. In light of these requirements, we propose a novel shrinkage process for sparse state space and TVP models. Building on the work of Cadonna et al. (2020) we leverage the desirable properties of the triple gamma prior and introduce a shrinkage process that aims to combine sufficient regularization with enough flexibility to capture salient features of the data. Links to the work of Kowal et al. (2019) are explored and an efficient MCMC algorithm is discussed.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title The Dynamic Triple Gamma Prior
Event Joint Statistical Meetings
Year 2021
Date 08.08-12.08
Country United States/USA
Location Seattle
URL https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/AbstractDetails.cfm?abstractid=317452

Associations

People
Knaus, Peter (Details)
Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1105 Computer software (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
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