Document Type

Article

Publication Date

1-1-1997

Abstract

Spatial estimators usually require the manipulation of n2 relations among n observations and use operations such as determinants, eigenvalues, and inverses whose operation counts grow at a rate proportional to n3. This paper provides ways to quickly compute estimates when the dependent variable follows a spatial autoregressive process, which by appropriate specification of the independent variables can subsume the case when the errors follow a spatial autoregressive process. Since only nearby observations tend to affect a given observation, most observations have no effect and hence the spatial weight matrix becomes sparse. By exploiting sparsity and rearranging computations, one can compute estimates at low cost. As a demonstration of the efficacy of these techniques the paper provides a Monte Carlo study whereby 3,107 observation regressions require only 0.1 seconds each when using Matlab on a 200 Mhz Pentium Pro personal computer. In addition, the paper illustrates these techniques by examining voting behavior across U.S. counties in the 1980 presidential election.

Publication Source (Journal or Book title)

Geographical Analysis

First Page

232

Last Page

247

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