Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330162 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 44 [Issue:] 7 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2025 [Pages:] 2055-2066
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
This paper provides a detailed assessment of the real‐time forecast accuracy of a wide range of vector autoregressive models that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed‐frequency time‐varying parameter vector autoregressive model with stochastic volatility. Monte Carlo simulation shows that the novel model is well‐suited to estimate missing monthly observations in an environment that is subject to parameter instability. In a real‐time forecast exercise, the model delivers accurate now‐ and forecasts and, on average, outperforms its competitors. Particularly, inflation and unemployment rate forecasts are more precise.
Subjects: 
Bayesian methods
forecasting
mixed‐frequency models
nowcasting
time‐varying parameters
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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