A Bayesian Non‐Parametric Dynamic AR Model for Multiple Time Series Analysis
提出一个贝叶斯非参数模型来分析多个时间序列，每个序列有自回归结构，通过随时间演化的共同误差分布来借用信息，并用墨西哥32州经济活动指数数据验证。
In this article, we propose a Bayesian non‐parametric model for the analysis of multiple time series. We consider an autoregressive structure of order p for each of the series and borrow strength across the series by considering a common error population that is also evolving in time. The error populations (distributions) are assumed non‐parametric whose law is based on a series of dependent Polya trees with zero median. This dependence is of order q and is achieved via a dependent beta process that links the branching probabilities of the trees. We study the prior properties and show how to obtain posterior inference. The model is tested under a simulation study and is illustrated with the analysis of the economic activity index of the 32 states of Mexico.