Spatial weights matrix selection and model averaging for multivariate spatial autoregressive models
针对多元空间自回归模型，当候选空间权重矩阵可能不包含真实矩阵时，提出模型选择与模型平均方法，并证明其渐近最优性，通过新浪微博数据验证了用户发帖行为受关注者影响。
.In this article, we focus on the model specification problem in multivariate spatial econometric models when a candidate set for the spatial weights matrix is available. We propose a model selection method for the multivariate spatial autoregressive model when the true spatial weights matrix may not be in the candidates. We show that the selected estimator is asymptotically optimal in the sense of minimizing the squared loss. If the candidate set contains the true spatial weights matrix, the method has selection consistency. We further propose a model averaging estimator that combines a set of candidate models and show its asymptotic optimality. Monte Carlo simulation results indicate that the proposed model selection and model averaging estimators perform quite well in finite samples. The proposed methods are applied to a Sina Weibo data to reveal how the user’s posting behavior is influenced by the users that he follows. The analysis results indicate that the influence tends to be uniformly distributed among the user’s followee, or linearly correlated with the number of followers of the followee.