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dc.contributor.authorCross, Jamie L.
dc.contributor.authorHou, Chenghan
dc.contributor.authorKoop, Gary
dc.contributor.authorPoon, Aubrey
dc.date.accessioned2021-06-23T13:15:54Z
dc.date.available2021-06-23T13:15:54Z
dc.date.issued2021-06-15
dc.identifier.issn1892-2198
dc.identifier.urihttps://hdl.handle.net/11250/2760927
dc.description.abstractVector autoregressions with stochastic volatility in both the conditional mean and variance are commonly used to estimate the macroeconomic effects of uncertainty shocks. Despite their popularity, intensive computational demands when estimating such models have made out-of-sample forecasting exercises impractical, particularly when working with large data sets. In this article, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior and predictive inference in such models that facilitates such exercises. The key insight underlying the algorithm is that the (log-)conditional densities of the log volatilities possess Hessian matrices that are banded. This enables us to build upon recent advances in band and sparse matrix algorithms for state space models. In a simulation exercise, we evaluate the new algorithm numerically and establish its computational and statistical efficiency over a conventional particle filter based algorithm. Using macroeconomic data for the US we find that such models generally deliver more accurate point and density forecasts over a conventional benchmark in which stochastic volatility only enters the variance of the model.en_US
dc.language.isoengen_US
dc.publisherBI Norwegian Business Schoolen_US
dc.relation.ispartofseriesCAMP Working Paper Series;04/2021
dc.subjectBayesian VARsen_US
dc.subjectMacroeconomic Forecastingen_US
dc.subjectStochastic Volatility in Meanen_US
dc.subjectState Space Modelsen_US
dc.subjectUncertaintyen_US
dc.titleMacroeconomic Forecasting with Large Stochastic Volatility in Mean VARsen_US
dc.typeWorking paperen_US
dc.source.pagenumber50en_US


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