A generalized moments estimator for the autoregressive parameter in a spatial model
A generalized moments estimator for the autoregressive parameter in a spatial model
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DOI：
10.1111/1468-2354.00027
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发表时间：
1999-05-01
影响因子：
1.5
通讯作者：
Prucha, IR
中科院分区：
文献类型：
--
作者：
Kelejian, HH;Prucha, IR
本文研究了一个广泛应用的空间自相关模型中自回归参数的估计问题。在文献中考虑的这个参数的典型估计是（准）最大似然估计对应于一个正常密度。然而，如本文所讨论的，（准）最大似然估计在许多情况下，涉及中等或大规模的样本可能是不可行的计算。在本文中，我们提出了一个广义矩估计，是计算简单的样本大小无关。我们提供的结果有关的大样本和小样本的性质，这个估计。
This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in this paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate- or large-sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator.