Dynamic Mixture Vector Autoregressions with Score-Driven Weights

Alexander Georges Gretener, Matthias Neuenkirch and Dennis Umlandt

No 2022-02, Working Paper Series from University of Trier, Research Group Quantitative Finance and Risk Analysis

Abstract: We propose a novel dynamic mixture vector autoregressive (VAR) model in which time-varying mixture weights are driven by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model in the subsequent period is increased if the current observation is more likely to be 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 the mixture dynamics from a variety of different data generating processes which most other observation-driven dynamic mixture VAR models cannot appropriately cope with. Finally, we illustrate our approach by an application where we model the conditional joint distribution of economic and financial conditions and derive generalized impulse responses.

Keywords: Dynamic Mixture Models; Generalized Autoregressive Score Models; Macro-Financial Linkages; Nonlinear VAR (search for similar items in EconPapers)
JEL-codes: C32 C34 G17 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2022
New Economics Papers: this item is included in nep-ets, nep-mac and nep-ore
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Working Paper: Dynamic Mixture Vector Autoregressions with Score-Driven Weights (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:trr:qfrawp:202202

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