Estimation of dynamic panel spatial vector autoregression: Stability and spatial multivariate cointegration

Abstract

This paper introduces dynamic panel spatial vector autoregressive models. We study features of dynamics and spatial interactions that an SVAR model can generate and classify the model into stable or unstable cases by partitioning parameter spaces. For stable, spatial cointegration, and mixed cointegration cases, we investigate identification and QML estimation of the models to take into account simultaneity and correlated relationships. Asymptotic properties and bias-corrected estimators are presented. To detect unknown cointegration relationships, we introduce a sequential likelihood ratio testing procedure. Simulations show the advantage of QMLEs on bias reduction and efficiency gains. The empirical application provides evidences on ancient China’s market integration.