Dynamic Hierarchical Models
从贝叶斯视角分析横截面时间序列数据，将分层线性模型与动态线性模型融合为统一框架，涵盖计量经济学和实验设计中的多种模型，并讨论推断、平滑及非线性扩展。
SUMMARY An analysis of a time series of cross-sectional data is considered under a Bayesian perspective. Information is modelled in terms of prior distributions and stratified parametric linear models developed by Lindley and Smith and dynamic linear models developed by Harrison and Stevens are merged into a general framework. This is shown to include many models proposed in econometrics and experimental design. Properties of the model are derived and shrinkage estimators reassessed. Evolution, smoothing and passage of data information through the levels of the hierarchy are discussed. Inference with an unknown scalar observation variance is drawn and an extension to the non-linear case is proposed.