Premium
Bayesian Model Averaging for Spatial Econometric Models
Author(s) -
LeSage James P.,
Parent Olivier
Publication year - 2007
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2007.00703.x
Subject(s) - markov chain monte carlo , econometrics , econometric model , autoregressive model , bayesian probability , statistics , bayesian inference , ordinary least squares , spatial econometrics , mathematics , population , demography , sociology
We extend the literature on Bayesian model comparison for ordinary least‐squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC 3 by Madigan and York is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin–destination population migration flows between the 48 U.S. states and the District of Columbia during the 1990–2000 period.