论文标题
依赖数据的高阶正确快速移动平均Bootstrap
A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data
论文作者
论文摘要
我们为依赖数据开发并实施了一种新颖的快速引导程序。我们的计划基于I.I.D.重新采样平滑的力矩指示器。我们表征该方法有效的参数和半参数估计问题。我们显示了所提出的程序的渐近改进，证明它在时间序列的轻度假设，估计功能和平滑核下是高阶正确的。我们说明了对广义经验可能性估计的程序的适用性和优势。作为副产品，我们的快速引导程序提供了高阶正确的渐近置信度分布。自回归条件持续时间模型上的蒙特卡洛模拟提供了数值证据，表明新型引导程序会产生更高阶的准确置信区间。当股票交易量的动力学上的真实数据应用说明了我们方法比常规应用的一阶渐近理论的优势，当测试统计量的基本分布偏向或脂肪尾时。
We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d. resampling of the smoothed moment indicators. We characterize the class of parametric and semi-parametric estimation problems for which the method is valid. We show the asymptotic refinements of the proposed procedure, proving that it is higher-order correct under mild assumptions on the time series, the estimating functions, and the smoothing kernel. We illustrate the applicability and the advantages of our procedure for Generalized Empirical Likelihood estimation. As a by-product, our fast bootstrap provides higher-order correct asymptotic confidence distributions. Monte Carlo simulations on an autoregressive conditional duration model provide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals. A real-data application on dynamics of trading volume of stocks illustrates the advantage of our method over the routinely-applied first-order asymptotic theory, when the underlying distribution of the test statistic is skewed or fat-tailed.
