论文标题
弱依赖数据的内核估计量的均匀率
Uniform Rates for Kernel Estimators of Weakly Dependent Data
论文作者
论文摘要
本文为绝对常规的固定过程的内核估计器提供了新的均匀速率结果，这些过程在带宽和无限尺寸类别的依赖变量和回归器中均匀。我们的结果对于在时间序列模型中为两步半参数估计器建立渐近理论很有用。我们应用结果以获得非参数估计及其对预期不足过程的比率。
This paper provides new uniform rate results for kernel estimators of absolutely regular stationary processes that are uniform in the bandwidth and in infinite-dimensional classes of dependent variables and regressors. Our results are useful for establishing asymptotic theory for two-step semiparametric estimators in time series models. We apply our results to obtain nonparametric estimates and their rates for Expected Shortfall processes.
