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Bayesian spatial probit estimation: a primer and an application to HYV rice adoption
Author(s) -
Holloway Garth,
Shankar Bhavani,
Rahmanb Sanzidur
Publication year - 2002
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/j.1574-0862.2002.tb00127.x
Subject(s) - bayesian probability , econometrics , probit , probit model , econometric model , estimation , bayes estimator , computer science , spatial econometrics , bayesian econometrics , multinomial probit , ranking (information retrieval) , economics , bayes' theorem , statistics , bayes factor , machine learning , mathematics , artificial intelligence , management
Increasingly, spatial econometric methods are becoming part of the standard toolkit of applied researchers in agricultural, environmental and development economics. Nonetheless, applications in discrete‐choice settings remain few and despite its appeal, applications of the Bayesian paradigm in these settings are still fewer. We provide a primer to the Bayesian spatial probit with the objective of making accessible to non‐users a class of iterative estimation methods that have become fairly routine in Bayesian circles, offer an extremely powerful addition to applied researchers toolkits, and are essential in Bayesian implementation of spatial econometric models. We demonstrate the methods and apply them to estimate the ‘neighbourhood effect’ in high‐yielding variety (HYV) adoption among Bangladeshi rice producers. We estimate the strength of this relationship using a standard, spatial probit model and compare the policy conclusions with and without the neighbourhood effect included.