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Estimating regression coefficients by W-based and latent variables spatial autoregressive models in the presence of spillovers from hotspots: Evidence from Monte Carlo simulations

机译：在热点存在溢出的情况下，基于W和潜在变量的空间自回归模型估算回归系数：蒙特卡洛模拟的证据

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The paper evaluates by means of Monte Carlo simulations the estimators of regression coefficients in the presence of spillover effects from one or more hotspots by the classical W-based spatial autoregressive model and the structural equation model with latent variables (SEM). The estimators are evaluated in terms of bias and root mean squared error (RMSE) for different values of the spatial autoregressive coefficient, different sample sizes and different specifications of weight matrices. The simulation results show that both approaches perform better for smaller values of the spatial autoregressive coefficient and larger sample sizes. SEM tends to outperform the classical approach in term of bias but the classical model based on first-order contiguity matrix has lowest RMSE in most cases. Furthermore, SEM provides a more stable performance in terms of variations of bias and RMSE with respect to changes in the value of autoregressive coefficient, sample size and number of hotspots. It follows that compared to the classical approach, SEM does not only have favorable behavioral properties in that it straightforwardly allows inclusion of different types of spatial dependence in one model framework and of testing distance decay, but also favorable econometric properties.
机译：本文通过蒙特卡洛模拟，通过经典的基于W的空间自回归模型和具有潜在变量（SEM）的结构方程模型，评估了一个或多个热点存在溢出效应时回归系数的估计量。针对不同的空间自回归系数值，不同的样本大小和权重矩阵的不同规范，根据偏差和均方根误差（RMSE）对估算器进行评估。仿真结果表明，两种方法对于较小的空间自回归系数值和较大的样本量均具有较好的性能。扫描电镜在偏见方面倾向于优于经典方法，但是在大多数情况下，基于一阶连续性矩阵的经典模型的RMSE最低。此外，就自回归系数的值，样本大小和热点数量的变化而言，SEM在偏差和RMSE的变化方面提供了更稳定的性能。因此，与经典方法相比，SEM不仅具有良好的行为特性，因为它可以直接在一个模型框架中包含不同类型的空间依赖关系并测试距离衰减，而且还具有良好的计量经济特性。

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