Bayesian Lassos for spatial durbin error model with smoothness prior: Application to detect spillovers of China's treaty ports

In this paper we consider a generalized spatial durbin error model with spatial spillovers that are flexible and subject to abrupt change arising from zero spillover effects. We propose a new class of Bayesian lasso-type prior, the Bayesian elastic net with smoothness prior, to both tackle the multicollinearity among spatial lags of explanatory variables and capture both zero and nonzero spillover effects. We develop a computationally tractable Markov Chain Monte Carlo (MCMC) algorithm to estimate the model under the new prior. We also study the corresponding model selection issue among the generalized spatial durbin error model and its two special cases: 1) the spatial error model and 2) the spatial autoregressive type model. Simulation results suggest that the new prior can outperform many existing Bayesian priors in terms of in-sample predictive performance. We apply the model with the new prior to investigate the influence and spillover effects of China's historical treaty ports in the 19th century on prefectures' population and economic growth in the long-run. We find that prefectures with treaty ports tend to grow faster in terms of population size and GDP level. We also detect positive and significant spillovers when one's neighbors become treaty ports.