Using the spatial configuration of the data to improve estimation

被引：149

作者：

Pace, RK ^{[1
]}

Gilley, OW ^{[1
]}

机构：

[1] LOUISIANA TECH UNIV,COLL ADM & BUSINESS,DEPT ECON & FINANCE,RUSTON,LA 71272

来源：

JOURNAL OF REAL ESTATE FINANCE AND ECONOMICS | 1997年 / 14卷 / 03期

关键词：

spatial autocorrelation; SAR; hedonic pricing;

D O I：

10.1023/A:1007762613901

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Using the well-known Harrison and Rubinfeld (1978) hedonic pricing data, this manuscript demonstrates the substantial benefits obtained by modeling the spatial dependence of the errors. Specifically, the estimated errors on the spatial autoregression fell by 44% relative to OLS. The spatial autoregression corrects predicted values by a nonparametric estimate of the error on nearby observations and thus mimics the behavior of appraisers. The spatial autoregression, by formally incorporating the areal configuration of the data to increase predictive accuracy and estimation efficiency, has great potential in real estate empirical work.

引用

页码：333 / 340

页数：8

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