Dynamic Mixture Vector Autoregressions with Score-Driven Weights

Alexander Georges Gretener, Matthias Neuenkirch and Dennis Umlandt

No 2022-02, Research Papers in Economics from University of Trier, Department of Economics

Abstract: We propose a novel dynamic mixture vector autoregressive (VAR) model in which the time-varying mixture weights are driven by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model is increased in the subsequent period if the current observation is more likely to have been drawn from this particular state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood-based estimation and inference. We conduct a Monte Carlo study and find that the score-driven mixture VAR model is able to adequately filter and predict the mixture dynamics from a variety of different data generating processes, which other observation-driven dynamic mixture VAR models cannot handle appropriately. Finally, the empirical performance of the approach is illustrated by two applications: (i) the conditional joint distribution of stock and bond returns, and (ii) the regime-dependent connection of economic and financial conditions.

Keywords: Dynamic Mixture Models; Generalized Autoregressive Score Models; Macro-Financial Linkages; Nonlinear Vector Autoregressions; Stock and Bond Return Dynamics (search for similar items in EconPapers)
JEL-codes: C32 C34 G17 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2022
New Economics Papers: this item is included in nep-ecm, nep-fdg and nep-ore
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http://www.uni-trier.de/fileadmin/fb4/prof/VWL/EWF/Research_Papers/2022-02.pdf Third version, 2024 (application/pdf)

Related works:
Working Paper: Dynamic Mixture Vector Autoregressions with Score-Driven Weights (2023)
Working Paper: Dynamic Mixture Vector Autoregressions with Score-Driven Weights (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:trr:wpaper:202202

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