带未知异方差广义空间模型的有效估计

空间单元大小以及其它的经济特征上的差异，常常会导致空间异方差问题。本文给出了广义空间模型异方差问题的三种不同估计方法。第一种方法是将异方差形式参数化，来克服自由度的不足，使用ML估计进行实现。而针对异方差形式未知时，分别采用了基于2SLS的迭代GMM估计和更加直接的MCMC抽样方法加以解决,特别是MCMC方法表现得更加优美。蒙特卡罗模拟表明，给定异方差形式条件下， ML估计通过异方差参数化的方法依然可以获得较好的估计效果。而异方差形式未知的情况下，另外两种方法随着样本数的增大时也可以与ML的估计结果趋于一致。

Efficient Estimation of General Spatial Model with Unknown Heteroscedasticity

The difference of spatial unit size and other economic characteristics often lead to spatial variance problem. This paper presents three kinds of estimation methods for heteroscedasticity problem of general spatial model. The first method is to use ML estimation to overcome the lack of degrees of freedom by parametric heteroscedastic form. When the forms of heteroskedasticity is unknown, we solve it by using the iterative GMM based on 2SLS estimation and more directly MCMC sampling method. And MCMC method is even more graceful. Monte Carlo simulation founds, the MLE is still a better estimate by parametric heteroscedastic style when the forms of heteroskedasticity is known. When the forms of heteroskedasticity is unknown, with increasing sample size, two other methods can be gradually consistent with ML estimation.