Dynamic Mixture Vector Autoregressions With Score‐Driven Weights
提出一种新的动态混合向量自回归模型，其混合权重由预测似然得分驱动，无需特定分布假设，便于基于似然的估计和推断，蒙特卡洛研究验证了其过滤和预测混合动态的能力。
ABSTRACT We propose a novel dynamic mixture vector autoregressive (VAR) model where the time‐varying mixture weights are driven by the predictive likelihood score. Intuitively, the weight of a component VAR model is increased in the subsequent period if the current observation is more likely to be drawn from this state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood‐based estimation and inference. In a Monte Carlo study, we document the model's ability to filter and predict mixture dynamics across different data‐generating processes. Moreover, we illustrate the model's empirical performance with the help of two applications.