机构地区: 华南理工大学经济与贸易学院
出 处: 《控制与决策》 2019年第12期2679-2689,共11页
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.
关 键 词: 层级数据空间经济计量模型 广义矩估计 可行的广义最小二乘估计 蒙特卡洛仿真
领 域: [理学—概率论与数理统计] [理学—数学]