Unpacking Spatial Dependence: A New Experimental Design for Spatial Autoregressive Simulation

Unpacking Spatial Dependence: A New Experimental Design for Spatial Autoregressive Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unpacking Spatial Dependence: A New Experimental Design for Spatial Autoregressive Simulation Wei Kang, Levi Wolf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8585449/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Simulating spatial patterns with varying levels of spatial autocorrelation is essential for evaluating models, estimators, and diagnostics. The spatial autoregressive (SAR) process is widely used for this purpose, but its main parameter affects both spatial autocorrelation and spatial heterogeneity. Only the former is usually acknowledged. Unfortunately, these unintended variance effects can bias Monte Carlo interpretations when ignored. This paper introduces a variance-stabilized SAR (VSSAR) process that decouples spatial autocorrelation from heteroskedasticity. The proposed approach preserves the spatial patterning implied by the SAR model while enforcing constant marginal variance, making it possible to more accurately assess the behavior of models and estimators under spatial autocorrelation. We validate the VSSAR method by replicating three canonical simulation experiments and demonstrate that it should serve as the new default data-generating process for simulation studies in spatial analysis when spatial autocorrelation is the primary object of interest. JEL codes: C1, C15, C21, C2 Spatial dependence spatial heteroskedasticity spatial autocorrelation Monte Carlo Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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