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Large mixed-frequency VARs with a parsimonious time-varying parameter structure

Tracking the slowdown in long-run GDP growth

Thomas B Götz and Klemens Hauzenberger

The Econometrics Journal, 2021, vol. 24, issue 3, 442-461

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)
Date: 2021
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