Series estimation under cross-sectional dependence
针对横截面数据中可能存在的依赖性和异质性，为非参数和半参数回归模型的级数估计建立了渐近理论，包括均方收敛速度和渐近正态性，并通过蒙特卡洛模拟和实证应用验证了有限样本性质。
An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic applications, and can apply also to panel, spatial and time series data. A mean square rate of convergence of nonparametric regression estimates is established followed by asymptotic normality of a quite general statistic. Data-driven studentizations that rely on single or double indices to order the data are justified. In a partially linear model setting, Monte Carlo investigation of finite sample properties and two empirical applications are carried out.