This paper introduces a novel wild bootstrap for dependent data (WBDD) as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on dependent heterogeneous data. The consistency of the bootstrap variance estimator for smooth function of the sample mean is shown to be robust against heteroskedasticity and dependence of unknown form. The first-order asymptotic validity of the WBDD in distribution approximation is established when data are assumed to satisfy a near epoch dependent condition and under the framework of the smooth function model. The WBDD offers a viable alternative to the existing non parametric bootstrap methods for dependent data. It preserves the second-order correctness property of blockwise bootstrap (provided we choose the external random variables appropriately), for stationary time series and smooth functions of the mean. This desirable property of any bootstrap method is not known for extant wild-based bootstrap methods for dependent data. Simulation studies illustrate the finite-sample performance of the WBDD.

本文介绍了一种新的依赖数据野生引导程序 (WBDD)，作为计算估计量标准误差和基于依赖异构数据为参数构建置信区域的一种方法。样本均值平滑函数的自举方差估计量的一致性被证明对异方差性和未知形式的依赖性具有鲁棒性。WBDD 在分布近似中的一阶渐近有效性是在假设数据满足近历元依赖条件并在平滑函数模型的框架下建立的。WBDD 为依赖数据的现有非参数引导方法提供了一种可行的替代方法。它保留了 blockwise bootstrap 的二阶正确性属性（前提是我们适当地选择了外部随机变量），对于固定时间序列和均值的平滑函数。对于依赖数据的现存的基于野生的引导方法，任何引导方法的这种理想属性都是未知的。模拟研究说明了 WBDD 的有限样本性能。