Regression modelGIS / spatial

Bayesian Spatial Durbin Model

The Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full posterior distributions for all parameters, quantifying uncertainty beyond what classical maximum-likelihood estimation offers.

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Sources

  1. LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
  2. LeSage, J. P. (2014). Spatial Econometric Panel Data Model Comparison Using Heterogeneous Panels with Local Spatial Spillovers. Empirical Economics, 46(1), 193–211. DOI: 10.1007/s00181-013-0683-4

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Referenced by

ScholarGateBayesian Spatial Durbin Model (Bayesian Spatial Durbin Model). Retrieved 2026-06-04 from https://scholargate.app/en/spatial-analysis/bayesian-spatial-durbin-model