Abstract
The choice of weights is a non-nested problem in most applied spatial econometric models. Despite numerous recent advances in spatial econometrics, the choice of spatial weights remains exogenously determined by the researcher in empirical applications. Bayesian techniques provide statistical evidence regarding the simultaneous choice of model specification and spatial weights matrices by using posterior probabilities. This paper demonstrates the Bayesian estimation approach in a spatial hedonic property model estimating the impacts of repeated wildfires on house prices in Southern California. We find that improper choice of spatial model and weights can result in up to 5 percent difference in estimated coefficients and in our case study up to a $15 Million difference in total benefits of reducing wildfires in Los Angeles County.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 245-255 |
| Number of pages | 11 |
| Journal | Review of Regional Studies |
| Volume | 40 |
| Issue number | 3 |
| State | Published - 2010 |
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Keywords
- Bayesian Estimation
- Spatial Hedonic Models
- Wildfires
ASJC Scopus subject areas
- Geography, Planning and Development
- Earth-Surface Processes
Cite this
Bayesians in space : Using Bayesian methods to inform choice of spatial weights matrix in hedonic property analyses. / Mueller, Julie M; Loomis, John B.
In: Review of Regional Studies, Vol. 40, No. 3, 2010, p. 245-255.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bayesians in space
T2 - Using Bayesian methods to inform choice of spatial weights matrix in hedonic property analyses
AU - Mueller, Julie M
AU - Loomis, John B.
PY - 2010
Y1 - 2010
N2 - The choice of weights is a non-nested problem in most applied spatial econometric models. Despite numerous recent advances in spatial econometrics, the choice of spatial weights remains exogenously determined by the researcher in empirical applications. Bayesian techniques provide statistical evidence regarding the simultaneous choice of model specification and spatial weights matrices by using posterior probabilities. This paper demonstrates the Bayesian estimation approach in a spatial hedonic property model estimating the impacts of repeated wildfires on house prices in Southern California. We find that improper choice of spatial model and weights can result in up to 5 percent difference in estimated coefficients and in our case study up to a $15 Million difference in total benefits of reducing wildfires in Los Angeles County.
AB - The choice of weights is a non-nested problem in most applied spatial econometric models. Despite numerous recent advances in spatial econometrics, the choice of spatial weights remains exogenously determined by the researcher in empirical applications. Bayesian techniques provide statistical evidence regarding the simultaneous choice of model specification and spatial weights matrices by using posterior probabilities. This paper demonstrates the Bayesian estimation approach in a spatial hedonic property model estimating the impacts of repeated wildfires on house prices in Southern California. We find that improper choice of spatial model and weights can result in up to 5 percent difference in estimated coefficients and in our case study up to a $15 Million difference in total benefits of reducing wildfires in Los Angeles County.
KW - Bayesian Estimation
KW - Spatial Hedonic Models
KW - Wildfires
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M3 - Article
VL - 40
SP - 245
EP - 255
JO - Review of Regional Studies
JF - Review of Regional Studies
SN - 1553-0892
IS - 3
ER -