 Structural Equation Models for Dealing With Spatial ConfoundingHauke Thaden and Thomas Kneib The American Statistician, 2018, vol. 72, issue 3, 239-252 Abstract: In regression analyses of spatially structured data, it is common practice to introduce spatially correlated random effects into the regression model to reduce or even avoid unobserved variable bias in the estimation of other covariate effects. If besides the response the covariates are also spatially correlated, the spatial effects may confound the effect of the covariates or vice versa. In this case, the model fails to identify the true covariate effect due to multicollinearity. For highly collinear continuous covariates, path analysis and structural equation modeling techniques prove to be helpful to disentangle direct covariate effects from indirect covariate effects arising from correlation with other variables. This work discusses the applicability of these techniques in regression setups, where spatial and covariate effects coincide at least partly and classical geoadditive models fail to separate these effects. Supplementary materials for this article are available online. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:72:y:2018:i:3:p:239-252 Ordering information: This journal article can be ordered from DOI: 10.1080/00031305.2017.1305290 Access Statistics for this article The American Statistician is currently edited by Eric Sampson More articles in The American Statistician from Taylor & Francis Journals |
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