Title: The Spatial Probit Model of Interdependent Binary
Outcomes: Estimation, Interpretation, and Presentation
Authors: Robert Franzese, Jude Hays
Entrydate: 2007-07-20 16:28:16
Keywords: Spatial Probit, Bayesian Gibbs-Sampler Estimator,
Recursive Importance-Sampling Estimator, Interdependence,
Diffusion, Contagion, Emulation
Abstract: We have argued and shown elsewhere the ubiquity and
prominence of spatial interdependence in political science
research and noted that much previous practice has neglected
this interdependence or treated it solely as nuisance to the
serious detriment of sound inference. Previously, we considered
only linear-regression models of spatial and/or spatio-temporal
interdependence. In this paper, we turn to binary-outcome
models. We start by stressing the ubiquity and centrality of
interdependence in binary outcomes of interest to political and
social scientists and note that, again, this interdependence has
been ignored in most contexts where it likely arises and that, in
the few contexts where it has been acknowledged, the endogeneity
of the spatial lag has not be recognized. Next, we explain some
of the severe challenges for empirical analysis posed by spatial
interdependence in binary-outcome models, and then we follow
recent advances in the spatial-econometric literature to suggest
Bayesian or recursive-importance-sampling (RIS) approaches for
tackling estimation. In brief and in general, the estimation
complications arise because among the RHS variables is an
endogenous weighted spatial-lag of the unobserved latent
outcome, y*, in the other units; Bayesian or RIS techniques
facilitate the complicated nested optimization exercise that
follows from that fact. We also advance that literature by
showing how to calculate estimated spatial effects (as opposed
to parameter estimates) in such models, how to construct
confidence regions for those (adopting a simulation strategy for
the purpose), and how to present such estimates effectively.
http://polmeth.wustl.edu/retrieve.php?id=715
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