A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence
提出两阶段方法分离时空数据中的共同因子与纯空间关联，先用横截面均值提取共同因子，再对去因子后的观测进行多重检验识别显著空间连接，并应用于美国大都市区房价变化分析。
Summary An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross‐unit averages to extract common factors (viewed as a source of strong cross‐sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de‐factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio‐temporal model for the de‐factored real house price changes and obtain significant evidence of spatial connections, both positive and negative. Copyright © 2015 John Wiley & Sons, Ltd.