Likelihood dominance spatial inference

Document Type

Article

Publication Date

1-1-2003

Abstract

Many users estimate spatial autoregressions to perform inference on regression parameters. However, as the sample size or the number of potential models rise, computational exigencies make exact computation of likelihood-based inferences tedious or even impossible. To address this problem, we introduce a lower bound on the likelihood ratio test that can allow users to conduct conservative maximum likelihood inference while avoiding the computationally demanding task of computing exact maximum likelihood point estimates. This form of inference, known as likelihood dominance, performs almost as well as exact likelihood inference for the empirical examples examined. We illustrate the utility of the technique by performing likelihood-based inference on parameters from a spatial autoregression involving 890,091 observations in less than a minute (given the spatial weight matrix).

Publication Source (Journal or Book title)

Geographical Analysis

First Page

133

Last Page

147

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