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.

中文翻译：

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空间计量模型贝叶斯估计的大样本性质

本文研究了经典统计框架中空间计量经济模型的后验概率密度和贝叶斯估计量的渐近性质。由于贝叶斯估计的计算优势，我们专注于具有空间自回归干扰项的高阶空间自回归模型。我们还研究了空间自回归 Tobit 模型的贝叶斯估计的渐近特性，作为非线性空间模型的一个例子。模拟研究表明，即使样本量很小或中等，参数的后验分布也很好地近似于正态分布，并且贝叶斯估计器具有令人满意的性能，正如经典大样本理论所预测的那样。