关键词: 时变系数, 空间自回归模型, 极大似然估计

Abstract:

This paper researches the time-varying coefficient spatial autoregression panel data model, whose error has heteroscedasticity over time but without time serial correlation. This paper proposes a maximum likelihood estimation (MLE) for this model and proves the consistency and asymptotic normality of this MLE method, which testifies the favorite large sample properties of the MLE method. Meanwhile, we use Monte Carlo method to simulate the small properties of the MLE method, which shows that the estimator has large bias when N is small while the bias becomes smaller and smaller over the growth of N. Furthermore, we also find that the bias will increase over the growth of T, which shows that the MLE method is more suitable for the short panel data with small T and large N.

Key words: Time-varying Coefficient, Spatial Autoregression Model, Maximum Likelihood Estimation