We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. Then, we...

This paper uses count and binary data models with an endogenous dummy variable, and propensity score matching to evaluate the effect of the subsidized health care program in Medellin (Colombia). The subsidized program, which primarily covers poor people, is found to have neither a significant...

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We take into account the endogeneity through a system of simultaneous equations where a conditional correlation between the...