A Bayesian approach for the estimation of weight matrices in spatial autoregressive models. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Krisztin, Tamás; Piribauer, Philipp
• Is Part Of: Spatial economic analysis. Volume 18:Issue 1(2023)
• Page Start: 44
• Page End: 63
• Publication Date: 2023-01-02
• Subjects: estimation of spatial weight matrix -- spatial econometric model -- Bayesian Markov chain Monte Carlo (MCMC) estimation -- Monte Carlo simulations -- COVID-19 pandemic; C11 -- C21 -- C23 -- C51; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2022.2095426
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: 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 T relative to spatial units N . 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.