空间自回归模型中参数的Bayes估计

The Bayes estimation of parameters of spatial autoregressive model

首先采用线性Bayes方法估计了空间自回归模型的参数,并在均方误差矩阵准则下研究了线性Bayes估计相对两步最小二乘估计的优良性.然后,使用Metropolis抽样算法实现了对空间自相关系数的估计.最后,通过模拟试验比较了线性Bayes估计与两步最小二乘估计的优缺点.

First,the linear Bayes was used to estimate the parameters of spatial autoregressive model,and the superiorities of the linear Bayes estimator over two-step least square estimator were studied in terms of the mean square error matrix(MSEM)criterion.Then,the estimation of spatial autocorrelation coefficient was implemented by Metropolis algorithm.Finally,the superiority of the linear Bayes estimation and two-step least square estimation was compared by simulation experiments.