A Bayesian Analysis of Autoregressive Time Series Panel Data
提出一个贝叶斯分层模型来分析自回归时间序列面板数据，开发了两种MCMC算法，并通过实例和模拟证明，对相似序列进行合并估计比单独估计更精确。
We describe a Bayesian hierarchical model to analyze autoregressive time series panel data. We develop two algorithms using Markov-chain Monte Carlo methods, a restricted algorithm that enforces stationarity or nonstationarity conditions on the series and an unrestricted algorithm that does not. Two examples show that restricting stationary series to be stationary provides no new information, but restricting nonstationary series to be stationary leads to substantial differences from the unrestricted case. These examples and a simulation study also show that, compared with inference based on individual series, there are gains in precision for estimation and forecasting when similar series are pooled.