X Marks the Spot

Unlocking the Treasure of Spatial-X Models

Bibliographic Data

ID6396239
AuthorsCameron Wimpy (0000-0002-2049-5229, Arkansas State University), Guy D Whitten (0000-0003-1595-5507, Mitchell Institute), Laron K Williams (0009-0009-6584-5534), Laron Williams (University of Missouri)
Year2021
Volume83
Issue2
Pages722-739
Publication date2021-04-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueThe Journal of Politics (JOURNAL)
Journal identifiersISSN: 0022-3816 • E-ISSN: 1468-2508
PublisherUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/710089
OpenAlexW3046157978
LanguageEN
Citations received11
References cited37

In recent years, political scientists have made extensive use of spatial econometric models to test a wide range of theories. In a review of spatial papers, we find that a majority of these studies use the spatial autoregressive (SAR) model. Although this is a powerful method that reveals inferences about diffusion processes, it is also highly restrictive and makes assumptions that often are not appropriate given the expressed theories. We contend that spatial-X (SLX) models are a better reflection of typical theories about spatial processes. Our simulations demonstrate that SLX models consistently retrieve the direct and indirect effects of covariates when the true data-generating process reflects other spatial processes. SAR models, however, tend to find phantom higher-order effects that are not present in the data. We further demonstrate how SLX models reveal heterogeneity in patterns of spatial dependence in countries' defense burdens that SAR models cannot discover

Autoregressive model · Covariate · Econometrics · Geography · Physics · Range (aeronautics · Remote sensing · Spatial analysis · Spatial dependence · Spatial ecology · Spatial econometrics · Statistical physics · Statistics · Computer Science · Economic Policies and Impacts · Fiscal Policy and Economic Growth · Mathematics · Spatial and Panel Data Analysis

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