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Intuitively, the weight of a component VAR model is increased in the subsequent period if the current observation is more likely to be drawn from this state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood\u2010based estimation and inference. In a Monte Carlo study, we document the model's ability to filter and predict mixture dynamics across different data\u2010generating processes. Moreover, we illustrate the model's empirical performance with the help of two applications.<\/jats:p>","DOI":"10.1002\/jae.3119","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T13:48:54Z","timestamp":1741700934000},"page":"455-470","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic Mixture Vector Autoregressions With Score\u2010Driven Weights"],"prefix":"10.1002","volume":"40","author":[{"given":"Alexander Georges","family":"Gretener","sequence":"first","affiliation":[{"name":"Institute for Quantitative Business and Economics Research University of Kiel  Kiel Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Neuenkirch","sequence":"additional","affiliation":[{"name":"Department of Economics Trier University, Trier and CESifo  Munich 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