Dynamic Mixture Vector Autoregressions with Score-Driven Weights

51 Pages Posted: 16 Feb 2022 Last revised: 27 Nov 2024

See all articles by Alexander Georges Gretener

Alexander Georges Gretener

University of Kiel - Institute for Quantitative Business and Economic Research (QBER)

Matthias Neuenkirch

University of Trier - Faculty of Economics; CESifo (Center for Economic Studies and Ifo Institute)

Dennis Umlandt

University of Innsbruck - Department of Banking and Finance

Multiple version iconThere are 2 versions of this paper

Date Written: November 27, 2024

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

JEL Classification: C32, C34, G17

Suggested Citation

Gretener, Alexander Georges and Neuenkirch, Matthias and Umlandt, Dennis, Dynamic Mixture Vector Autoregressions with Score-Driven Weights (November 27, 2024). Available at SSRN: https://ssrn.com/abstract=4035650 or http://dx.doi.org/10.2139/ssrn.4035650

Alexander Georges Gretener

University of Kiel - Institute for Quantitative Business and Economic Research (QBER) ( email )

Heinrich-Hecht-Platz 9
Kiel, 24106
Germany
+49 (0) 431 / 880 - 5598 (Phone)

HOME PAGE: http://www.qber.uni-kiel.de

Matthias Neuenkirch (Contact Author)

University of Trier - Faculty of Economics ( email )

Universitätsring 15
Trier, 54296
Germany
+49 - (0)651 - 201 - 2629 (Phone)

HOME PAGE: http://www.uni-trier.de/index.php?id=50130

CESifo (Center for Economic Studies and Ifo Institute) ( email )

Poschinger Str. 5
Munich, DE-81679
Germany

Dennis Umlandt

University of Innsbruck - Department of Banking and Finance ( email )

Innsbruck
Austria

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
124
Abstract Views
594
Rank
349,997
PlumX Metrics