Dynamic shrinkage in time-varying parameter stochastic volatility in mean models

Florian Huber, Michael Pfarrhofer*

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikel

Abstract

Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
OriginalspracheEnglisch
FachzeitschriftarXiv
Jahrgang2005.06851
Ausgabenummerv1
PublikationsstatusVeröffentlicht - 15 Mai 2020

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