Spatial Autoregression with an Unknown Transformation of the Weight Matrix

Spatial Autoregression with an Unknown Transformation of the Weight Matrix | 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 Spatial Autoregression with an Unknown Transformation of the Weight Matrix Robert Daniel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7624842/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This paper extends the spatial autoregressive model with a known matrix analytic transformation of the weight matrix to the case where this transformation is unknown given that $T$ is large. As $n$ is fixed, this can be done efficiently without the use of full nonparametric methods. We analyze the quasi-maximum likelihood estimator and show its consistency and asymptotic normality. In simulation, our estimator can accurately construct the transformed spatial weights matrix, and we apply our procedure to the estimation of unemployment in New Hampshire with county based spatial dependence. JEL Classification: C1, C5 Spatial Econometrics Spatial Autoregression Matrix Analytic Functions Unemployment Full Text Additional Declarations No competing interests reported. Supplementary Files SubmissionSimulationCode.html Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 21 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 15 Sep, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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