Salīdzināt metodes

	Bayesian Spatial Lag Model	Bayesiešu telpiskais Durbina modelis
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1997	2009
Autors	LeSage (1997); fully elaborated in LeSage & Pace (2009)	LeSage & Pace
Tips	Bayesian spatial regression	Bayesian spatial regression
Pirmavots	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
Citi nosaukumi	Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM	Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM
Saistītās	5	6
Kopsavilkums	The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho.	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.