PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS
提出一种结合空间依赖与嵌套结构的面板数据模型，用广义矩估计法估计空间自回归参数和方差成分，并通过蒙特卡洛模拟验证其优于最大似然估计，最后应用于英格兰房价数据。
ABSTRACT This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane‐Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties.