论文标题
测试空间重量矩阵中空间动态面板数据模型中的内生性矩阵
Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models
论文作者
论文摘要
我提出了强大的RAO分数（RS）测试统计量，以确定空间权重矩阵中空间动态面板数据（SDPD）模型（QU，Lee和Yu，2017）中的内生性。我首先引入了偏置校正的分数函数，因为由于双向固定效果，得分函数并未围绕零左右。我进一步调整了分数函数，以纠正在同时依赖性，依赖于空间，随时间或空间时间依赖性的局部依赖性中的局部错误指定下的零假设的过度拒绝。然后，我得出了测试统计量的明确形式。蒙特卡洛模拟支持分析，并显示出良好的有限样品特性。最后，使用Penn World Table 6.1版中的数据提供了经验说明。
I propose Robust Rao's Score (RS) test statistic to determine endogeneity of spatial weights matrices in a spatial dynamic panel data (SDPD) model (Qu, Lee, and Yu, 2017). I firstly introduce the bias-corrected score function since the score function is not centered around zero due to the two-way fixed effects. I further adjust score functions to rectify the over-rejection of the null hypothesis under a presence of local misspecification in contemporaneous dependence over space, dependence over time, or spatial time dependence. I then derive the explicit forms of our test statistic. A Monte Carlo simulation supports the analytics and shows nice finite sample properties. Finally, an empirical illustration is provided using data from Penn World Table version 6.1.
