Dynamic mixture vector autoregressions with score-driven weights

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Research Papers in Economics ; No. 2/22

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Truncated and Censored Models; Switching Regression Models ; Financial Forecasting and Simulation
• Thema: Dynamic Mixture Models ; Generalized Autoregressive Score Models ; Macro-Financial Linkages ; Nonlinear VAR

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Gretener, Alexander Georges ; Neuenkirch, Matthias ; Umlandt, Dennis
• Ereignis: Veröffentlichung
• (wer): Universität Trier, Fachbereich IV - Volkswirtschaftslehre
• (wo): Trier
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/272353
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ