A Spatial Prior for Bayesian Vector Autoregressive Models
提出一种受空间自回归模型启发的贝叶斯先验，用于包含空间变量的时间序列向量自回归预测，相比Minnesota先验显著提升预测精度，并开发了基于空间邻接的先验均值，易于通过Theil-Goldberger估计实现。
In this paper we develop a Bayesian prior motivated by cross‐sectional spatial autoregressive models for use in time‐series vector autoregressive forecasting involving spatial variables. We compare forecast accuracy of the proposed spatial prior to that from a vector autoregressive model relying on the Minnesota prior and find a significant improvement. In addition to a spatially motivated prior variance as in LeSage and Pan (1995) we develop a set of prior means based on spatial contiguity. A Theil‐Goldberger estimator may be used for the proposed model making it easy to implement.