18132 6 98397 2 302473 document 98397 A Bayesian approach for the estimation of weight matrices in spatial autoregressive models.pdf application/pdf 329ed7f77fcf37bb5e6e0a7fdf6aa8c1 MD5 1518967 2022-07-28 10:42:20 https://pure.iiasa.ac.at/id/eprint/18132/1/A%20Bayesian%20approach%20for%20the%20estimation%20of%20weight%20matrices%20in%20spatial%20autoregressive%20models.pdf 18132 1 1 application/pdf text en public cc_by_4

A Bayesian approach for the estimation of weight matrices in spatial autoregressive models.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/98397 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/98397 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/98397 archive 353 disk0/00/01/81/32 2022-07-28 10:43:50 2022-07-28 10:43:50 2022-07-28 10:43:50 article show Krisztin T. 2088 0000-0002-9241-8628 Piribauer P. pub BNR IBF Bayesian Markov chain Monte Carlo (MCMC) estimation; COVID-19 pandemic; estimation of spatial weight matrix; Monte Carlo simulations; spatial econometric model We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number of time periods (Formula presented.) relative to spatial units (Formula presented.). When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic. 2022-07-22 published Routledge doi:10.1080/17421772.2022.2095426 167 public Spatial Economic Analysis 1-20 TRUE 1742-1772 no info:eu-repo/semantics/article