摘要
本文提出了广义空间信息准则,以解决广义嵌套空间模型的变量选择问题。依据大样本性质的不同,将该准则分为两类:空间AIC类准则和空间BIC类准则。研究发现,空间AIC类准则能有效解决空间模型中变量的错选和漏选问题,但存在多选变量的倾向;而空间BIC类准则能同时解决空间模型中变量的错选、漏选和多选问题,而且在特殊条件下能更有效解决错选和漏选问题,但往往需要更大的样本容量。Monte Carlo模拟结果印证了上述相关结论。最后,本文以城市对外资银行的吸引力为例,在给定测度指标的基础上,验证其空间相关性,并利用本文提出的方法对其影响因素进行变量选择。
General nesting spatial （GNS） model is the general form of spatial econometrics model. This paper studies variable selection problems of the model by raising the general spatial information criterion （GSIC）. According to the asymptotic behavior, GSIC can be classified into two classes spatial AIC-like and spatial BIC-like. It finds that spatial AIC-like criterions can solve the problem of selecting wrong variables or omitting variables, but trend to overfit, while spatial BIC-like criterions can solve all problem simultaneously and even do better in dealing with the problem of selecting wrong variables or omitting variables, but it usually need larger sample size. The Monte Carlo simulation also shows the above conclusion. In the end of the paper, it uses the ability of the city to attract foreign banks as the example. By giving measurement index, it demonstrates the spatial correlation, and uses the above methods to select factors of the above index.
作者
张元庆
陶志鹏
Zhang Yuanqing Tao Zhipeng
出处
《统计研究》
CSSCI
北大核心
2017年第9期100-107,共8页
Statistical Research
基金
教育部人文社科青年基金"变系数空间面板数据模型变量选择方法及应用"(15YJC790150)
国家统计局全国统计科学研究重点项目"时空经济系统半参数建模方法及其应用"(2016LZ16)
国家自然科学基金面上项目"我国环保产业R&D投入的决策理论与评价方法研究"(71673189)资助
关键词
空间计量
变量选择
大样本性质
地理区位
Spatial Econometrics
Variable Selection
Large Sample Properties
Geographical Location
