SOE
Chow Institute
User Center
中
EN
About WISE
People
Committee of Academic Consultants
Faculty Directory
Staff Directory
Research
Publications
Working Papers
Facilities&Centers
Education
Overview
Undergraduate Programs
Graduate Programs
Study-Abroad MA Programs
Exchange Programs
Executive Education
News & Events
News
Announcements
Conferences
Seminars & Conferences
Job Openings
SOE
Chow Institute
User Center
中
EN
About WISE
Introduction to WISE
Contact Us
Map and Direction
People
Committee of Academic Consultants
Faculty Directory
Staff Directory
Research
Publications
Working Papers
Facilities&Centers
Education
Overview
Undergraduate Programs
Graduate Programs
Study-Abroad MA Programs
Exchange Programs
Executive Education
News & Events
News
Announcements
Conferences
Seminars & Conferences
Job Openings
Research
Home
->
Research
->
Publications
->
Content
Research
Publications
Working Papers
Facilities&Centers
Finance & Economics Experimental Lab
MOE Key Lab in Econometrics
Fujian Provincial Key Lab in Statistics
Center for Econometrics Research
Center for Financial Research
Center for Research in Labor Economics
Center for Macroeconomics Research
Center for Statistics Research
Center for Information Technology
SAS Center for Excellence in Econometrics
High-Speed Computing Cluster
Bayesian Lassos for Spatial Durbin Error Model with Smoothness Prior: Application to Detect Spillovers of China’s Treaty Ports
Id:2562
Date:20200222
Status:published
ClickTimes:
作者
Jianan Li, Xiaoyi Han
正文
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.
JEL-Codes:
关键词:
Generalized spatial durbin error model; Bayesian elastic net with smoothness prior; Bayesian estimation; Spillover; Historical treaty port
TOP