LARGE SAMPLE PROPERTIES OF BAYESIAN ESTIMATION OF SPATIAL ECONOMETRIC MODELS
研究了空间自回归模型和空间自回归Tobit模型中贝叶斯估计的渐近性质，模拟表明小样本下后验分布近似正态且估计表现良好。
This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models. Simulation studies show that even when the sample size is small or moderate, the posterior distribution of parameters is well approximated by a normal distribution, and Bayesian estimators have satisfactory performance, as classical large sample theory predicts.