Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272353 
Year of Publication: 
2022
Series/Report no.: 
Research Papers in Economics No. 2/22
Publisher: 
Universität Trier, Fachbereich IV - Volkswirtschaftslehre, Trier
Abstract: 
We propose a novel dynamic mixture vector autoregressive (VAR) model in which timevarying 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.
Subjects: 
Dynamic Mixture Models
Generalized Autoregressive Score Models
Macro-Financial Linkages
Nonlinear VAR
JEL: 
C32
C34
G17
Document Type: 
Working Paper

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