This study investigates and quantifies the effect of different specifications of the spatial weights matrix (��) on estimates and inferences in the context of a regression model using the lattice perspective with polygon-type data. The study also investigates an alternative to the specification of �� by estimating a spatial variance-covariance matrix based on known features of the spatial data. Previous literature has addressed the a priori construction of �� and selection criteria but assumes point-type data. This study’s primary contribution is the setup of a true and known benchmark that allows the comparison of the different specifications of ��. This is accomplished by using a disaggregate point-type data generating process which is then aggregated into polygon-type data. Monte Carlo simulations show that current specifications of �� used in maximum likelihood estimation for the spatial error model perform poorly. Additionally, the estimated spatial variance-covariance matrix outperforms the traditional specifications of ��.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6393 |
| Date | 10 December 2021 |
| Creators | Kent, Cannon |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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