为解决因变量空间滞后存在的局域性问题，对现有常系数空间滞后的混合地理加权回归模型作了具有更广泛形式的拓展，提出一类变系数空间滞后的混合地理加权回归(MGWR-VSLR)模型。MGWR-VSLR模型实现了空间相关性与空间异质性融合，涵盖了绝大多数地理加权回归的模型形式，基于重构参数化方法和似然比检验分别给出模型的系数估计方法与显著性检验以及选取变系数的判别检验。在蒙特卡罗模拟与实际应用中，MGWR-VSLR模型均表现出优异的因变量拟合与预测能力。MGWR-VSLR模型的提出为定量化研究空间效应问题设定适宜的模型形式提供了支撑依据。
Spatial correlation and spatial heterogeneity are the theoretical basis of spatial econometrics. In order to solve the local problem of spatial lag of dependent variables, this study extended the existing mixed geographically weighted regression model with constant-coefficient spatial lag, and proposed a mixed geographically weighted regression model with varying-coefficient spatial lag. The mixed geographically weighted regression model with varying-coefficient spatial lag combines spatial correlation with spatial heterogeneity, and covers most of the model forms of geographically weighted regression. Based on the parameterization reconstruction method and likelihood ratio test, the coefficient estimation method, significance test of this model and the discriminant test of varying-coefficient are given respectively. Both in Monte Carlo simulation and practical application, the results show that the mixed geographically weighted regression model with varying-coefficient spatial lag renders itself well for the fitting and forecasting effect on dependent variable. The mixed geographically weighted regression model with varying-coefficient spatial lag provides a support for setting up a suitable model form for quantitative research on spatial effects.
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