Instrumental variable estimation of a spatial dynamic panel model with endogenous spatial weights whenTis small
针对空间权重可能由经济因素决定的内生性问题，提出短时间维度下允许空间权重内生且时变的空间动态面板模型，用两阶段工具变量估计并证明其一致性，蒙特卡洛模拟和省级政府支出数据验证了方法有效性。
The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension, where the spatial weights can be endogenous and time‐varying. We establish the consistency and asymptotic normality of the two‐stage instrumental variable (2SIV) estimator and we investigate its finite sample properties using a Monte Carlo simulation. When applying this model to study government expenditures in China, we find strong evidence of spatial correlation and time dependence in making spending decisions among China's provincial governments.