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Large mixed-frequency VARs with a parsimonious time-varying parameter structure Tracking the slowdown in long-run GDP growth

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fectj%2Futab001;h=repec:oup:emjrnl:v:24:y:2021:i:3:p:442-461.
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Authors
Thomas B Götz and Klemens Hauzenberger
Abstract
SummaryIn order to simultaneously consider mixed-frequency time series their joint dynamics and possible structural change we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.
Keywords
Bayesian methods ; time-varying intercepts ; common stochastic volatility ; forecasting ; real-time data ; COVID-19 case study (search for similar items in EconPapers)
Year Published
2021
Series
Econometrics Journal 2021 vol. 24 issue 3 442-461
Rank
0.68
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