SPATIAL DEPENDENCE AND SPATIAL STRUCTURAL INSTABILITY IN APPLIED REGRESSION ANALYSIS*
研究了当截面方程存在空间误差自相关时，传统Chow检验和似不相关回归（SUR）框架不再适用的问题，通过蒙特卡洛实验比较了传统检验、稳健方法、最大似然法和预检验技术的表现。
ABSTRACT The stability of regression coefficients over the observation set (“regional homogeneity”) is typically assessed by means of a Chow test or within a seemingly unrelated regression (SUR) framework. When spatial error autocorrelation is present in cross‐sectional equations the traditional tests are no longer applicable. I evaluate this both in formal terms as well as empirically. I introduce a taxonomy of spatial effects in models for structural instability, and discuss its implication for testing. I compare the performance of traditional tests, robust approaches, maximum‐likelihood procedures and pretest techniques by means of a series of simple Monte Carlo experiments.