scholarly journals Computational Issues in the Estima tion of the Spatial Probit Model: A Comparison of Various Estimators

2013 ◽  
Author(s):  
Anna Gloria Billé
Keyword(s):  
Probit Model ◽  
Spatial Probit

A Spatial Probit Modeling Approach to Account for Spatial Spillover Effects in Dichotomous Choice Contingent Valuation Surveys

2013 ◽  
Vol 45 (1) ◽  
pp. 53-63 ◽  
Author(s):  
John B. Loomis ◽  
Julie M. Mueller
Keyword(s):  
Contingent Valuation ◽  
Voting Behavior ◽  
Contingent Valuation Method ◽  
Probit Model ◽  
Spillover Effects ◽  
Dichotomous Choice ◽  
Spatially Correlated ◽  
Valuation Method ◽  
Spatial Probit ◽  
Unbiased Estimates

We present a demonstration of a Bayesian spatial probit model for a dichotomous choice contingent valuation method willingness-to-pay (WTP) questions. If voting behavior is spatially correlated, spatial interdependence exists within the data, and standard probit models will result in biased and inconsistent estimated nonbid coefficients. Adjusting sample WTP to population WTP requires unbiased estimates of the nonbid coefficients, and we find a $17 difference in population WTP per household in a standard vs. spatial model. We conclude that failure to correctly model spatial dependence can lead to differences in WTP estimates with potentially important policy ramifications.

A spatial probit model for fine-scale mapping of disease genes

2007 ◽  
Vol 31 (3) ◽  
pp. 252-260 ◽  
Author(s):  
Maria De Iorio ◽  
Claudio J. Verzilli
Keyword(s):  
Probit Model ◽  
Disease Genes ◽  
Fine Scale ◽  
Spatial Probit ◽  
Fine Scale Mapping

scholarly journals Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261737
Author(s):  
Jong Wook Lee ◽  
So Young Sohn
Keyword(s):  
Spatial Autocorrelation ◽  
Default Risk ◽  
Predictive Power ◽  
Credit Scoring ◽  
Probit Model ◽  
Peer To Peer ◽  
Empirical Results ◽  
Default Prediction ◽  
Spatial Probit ◽  
Relational Network

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.

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