Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Micro-Level Data

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Abstract
The application of spatial econometrics techniques to microlevel data of firms or households is problematic because of potentially large sample sizes and more-complicated spatial weight matrices. This paper provides the first application to actual household-level data of a new generalized-moments (GM) estimation technique developed by Kelejian and Prucha. The results based on this method, which is computationally feasible for any size data set, track those generated from the more conventional maximum-likelihood approach. The GM approach is shown to have the added advantage of easily allowing estimation of a more flexible functional form for the spatial weight matrix. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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