Abstract
We consider a threshold spatial Durbin model that allows for threshold effects in both endogenous and exogenous spatial interactions among cross-sectional units. We develop a computationally tractable Markov Chain Monte Carlo (MCMC) algorithm to estimate the model. We also propose a nested model selection procedure to test for spatial threshold effects, based upon the Bayes factor computed from the Savage–Dickey Density Ratio in Verdinelli and Wasserman (1995). Simulation studies suggest that the Bayesian estimator is more precise than the spatial 2SLS (S2SLS) estimator in Deng (2018). The model selection procedure works well when the sample size increases and the difference between spatial parameters enlarges.
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