论文标题
非组织时间序列的功能估计和变化检测
Functional estimation and change detection for nonstationary time series
论文作者
论文摘要
时间序列中结构中断的测试理想情况下应对关注参数的断裂敏感，同时对令人讨厌的变化进行稳健。因此，统计分析需要在无变化的零假设下允许某种形式的非平稳性。在本文中，构建了局部固定时间序列的集成参数的估计量，并建立了相应的功能中心限制定理，从而在轻度假设下为广泛参数的更改点推断。所提出的框架涵盖了所有参数，这些参数可以表示为矩的非线性函数，例如线性回归模型中的峰度，自相关和系数。为了基于派生的极限分布执行可行的推理，提出了引导程序变体并建立了其一致性。通过模拟研究和高频资产价格的应用来说明该方法。
Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus needs to allow for some form of nonstationarity under the null hypothesis of no change. In this paper, estimators for integrated parameters of locally stationary time series are constructed and a corresponding functional central limit theorem is established, enabling change-point inference for a broad class of parameters under mild assumptions. The proposed framework covers all parameters which may be expressed as nonlinear functions of moments, for example kurtosis, autocorrelation, and coefficients in a linear regression model. To perform feasible inference based on the derived limit distribution, a bootstrap variant is proposed and its consistency is established. The methodology is illustrated by means of a simulation study and by an application to high-frequency asset prices.
