A generalized moments estimator for the autoregressive parameter in a spatial model

• 成果类型: Article
• 署名作者: Kelejian, HH; Prucha, IR
• 署名单位: University System of Maryland; University of Maryland College Park
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/1468-2354.00027
• 发表日期: 1999
• 页码: 509-533
• 关键词: maximum-likelihood estimation; dependent observations; regression; variables

摘要

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.