Spatial panel data models with structural change

Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-$T$ and fixed-$T$ setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance