摘要
在回顾层级数据空间滞后(HSLAG)模型和层级数据空间误差自回归(HSEAR)模型的基础上,构建可同时考虑数据空间误差局部冲击效应与嵌套随机效应的层级数据空间误差移动平均(HSEMA)模型.在广义矩(GMM)估计的框架下,推导出HSEMA模型的18个矩条件元素,并得到各参数的估计量.通过蒙特卡洛仿真实验对比HSEMA模型、HSLAG模型和HSEAR模型各估计量的估计残差分布,以衡量各估计量的估计精度,并比较其有限样本性质.
This article reviews the existing hierarchically spatial lag(HSLAG) model and the hierarchically spatial autoregressive error(HSEAR) model, then builds up a hierarchically spatial moving average error(HSEMA) model that incorporates the spatial moving average error and the nested random effect. In the framework of generalized moment(GMM) estimation, 18 moment conditions are derived and the corresponding estimators are proposed for the HSEMA model. Furthermore, in order to investigate the precision and finite sample properties of each estimator, Monte Carlo simulation is conducted to make comparisons among the estimation residual distribution of HSEMA, HSLAG, and HSEAR models.
作者
叶倩婷
龙志和
林光平
梁华杰
YE Qian-ting;LONG Zhi-he;LIN Kuan-pin;LIANG Hua-jie(School of Economic and Commerce,South China University of Technology,Guangzhou 510006,China;Bank of Dongguan,Dongguan 523000,China;School of Business Administration,South China University of Technology,Guangzhou 510006,China;Department of Economics,Portland State University,Portland 97207,United States)
出处
《控制与决策》
EI
CSCD
北大核心
2019年第12期2679-2689,共11页
Control and Decision
基金
广东省自然科学基金项目(2015A030313216)
广东省教育厅特色创新项目(2014WTSCX001)
广州市哲学社会科学发展“十二五”规划课题项目(15G05)
中央高校基本科研业务费专项资金项目(XZD19)
国家留学基金项目(201606150038)
中国博士后科学基金项目(2019M652913)
