scholarly journals Transitions at Different Moments in Time: A Spatial Probit Approach

2016 ◽  
Vol 32 (2) ◽  
pp. 422-439 ◽  
Author(s):  
J. Paul Elhorst ◽  
Pim Heijnen ◽  
Anna Samarina ◽  
Jan P. A. M. Jacobs
Keyword(s):  
Spatial Probit

Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months

2020 ◽  
pp. 1471082X2096715
Author(s):  
Roger S. Bivand ◽  
Virgilio Gómez-Rubio
Keyword(s):  
Hurricane Katrina ◽  
Survival Data ◽  
R Package ◽  
American Statistical Association ◽  
Survival Models ◽  
Royal Statistical Society ◽  
Business Decisions ◽  
Modeling And Analysis ◽  
Probit Models ◽  
Spatial Probit

Zhou and Hanson; Zhou and Hanson; Zhou and Hanson ( 2015 , Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b , Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles ( LeSage etal., 2011a , Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.

scholarly journals Estimating Spatial Probit Models in R

The R Journal ◽  
2013 ◽  
Vol 5 (1) ◽  
pp. 130 ◽  
Author(s):  
Stefan Wilhelm ◽  
Miguel,Godinho,de Matos
Keyword(s):  
Probit Models ◽  
Spatial Probit

scholarly journals Spatial Probit and the Geographic Patterns of State Lotteries

2003 ◽  
Author(s):  
Cletus C. Coughlin ◽  
Thomas A. Garrett ◽  
Rubén Hernández-Murillo
Keyword(s):  
State Lotteries ◽  
Geographic Patterns ◽  
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.

Partial maximum likelihood estimation of spatial probit models

2013 ◽  
Vol 172 (1) ◽  
pp. 77-89 ◽  
Author(s):  
Honglin Wang ◽  
Emma M. Iglesias ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Probit Models ◽  
Spatial Probit
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