Statistics for Spatial Data.

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.

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A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

Under the hood Issues in the specification and interpretation of spatial regression models

This article outlines a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways. The point of departure is a reduced form in which local or globa...

Spatial Externalities, Spatial Multipliers, And Spatial Econometrics

Measuring the benefits of air quality improvement: a spatial hedonic approach

Spatial autocorrelation and neighborhood quality

Spatial autocorrelation occurs when population members are related through their geographic location. This paper presents a maximum likelihood procedure for simultaneously estimating the parameters of the correlation function and the regression coefficients. A test for the presence of spatial autocorrelation is also provided. Estimation of an hedonic regression illustrates the technique. Copyright 1988 by MIT Press.

https://www.jstor.org/stable/info/1926785

Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms

Spatial Autocorrelation: A Primer

This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation methods, and prediction procedures. An empirical example is provided in the appendix.

Spatial Autoregression Techniques for Real Estate Data

It is often necessary to accurately predict the price of a house between sales. One method of predicting house values is to use data on the characteristics of the area's housing stock to estimate a hedonic regression, using ordinary least squares (OLS) as the statistical technique. The coefficients of this regression are then used to produce the predicted house prices. However, this procedure ignores a potentially large source of information regarding house prices—the correlations existing between the prices of neighboring houses. The purpose of this article is to show how these correlations can be incorporated when estimating regression coefficients and when predicting house prices. The practical difficulties inherent in using a technique called kriging to predict house prices are discussed. The article concludes with an example of the procedure using multiple listings data from Baltimore.

Robin A. Dubin

Papers

Spatial autocorrelation and neighborhood quality

Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms

Spatial Autocorrelation: A Primer

Spatial Autoregression Techniques for Real Estate Data

Predicting House Prices Using Multiple Listings Data