Comparison of the blocked sampling approaches for the spatial dynamic panel data model

Comparison of the blocked sampling approaches for the spatial dynamic panel data model | 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 Comparison of the blocked sampling approaches for the spatial dynamic panel data model Yoshihiro Ohtsuka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9600276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study proposes a posterior sampling algorithm for a spatial dynamic panel data model using Bayesian inference. The model's stationarity depends on three parameters: simultaneous and lagged spatial dependencies, and serial correlation. Previous approaches sampled interdependent parameters individually using the random walk Metropolis-Hastings (RW-MH) algorithm, leading to high autocorrelation in the posterior samples and necessitating extensive simulations. To improve sampling efficiency, we developed a Bayesian estimation algorithm for these parameters that employs a Taylor approximation and a blocked MH (TaB-MH) algorithm. The TaB-MH algorithm demonstrates superior performance compared to the RW-MH algorithm and the No-U-Turn sampler, as shown by both simulated and empirical data. Markov chain Monte Carlo method No-U-Turn sampler Spatial dynamic panel data model Taylor approximation and blocked Metropolis-Hastings algorithm Full Text Additional Declarations No competing interests reported. Supplementary Files SupplmentaryMaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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