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Large stochastic volatility in mean VARs

Cross, Jamie, Hou, Chenghan, Koop, Gary, Poon, Aubrey (2023) Large stochastic volatility in mean VARs. Journal of Econometrics, 236 (1). Article Number 105469. ISSN 0304-4076. (doi:10.1016/j.jeconom.2023.05.006) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:103833)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1016/j.jeconom.2023.05.006

Abstract

Bayesian vector autoregressions with stochastic volatility in both the conditional mean and variance (SVMVARs) are widely used for studying the macroeconomic effects of uncertainty. Despite their popularity, intensive computational demands when estimating such models has constrained researchers to specifying a small number of latent volatilities, and made out-of-sample forecasting exercises impractical. In this paper, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm that facilitates timely posterior and predictive inference with large SVMVARs. In a simulation exercise, we show that the new algorithm is significantly faster than the state-of-the-art particle Gibbs with ancestor sampling algorithm, and exhibits superior mixing properties. In two applications, we show that large SVMVARs are generally useful for structural analysis and out-of-sample forecasting, and are especially useful in periods of high uncertainty such as the Great Recession and the COVID-19 pandemic.

Item Type: Article
DOI/Identification number: 10.1016/j.jeconom.2023.05.006
Subjects: H Social Sciences
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Funders: Örebro University (https://ror.org/05kytsw45)
Depositing User: Aubrey Poon
Date Deposited: 09 Nov 2023 12:01 UTC
Last Modified: 10 Nov 2023 10:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/103833 (The current URI for this page, for reference purposes)

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