论文标题
推断非平稳的重尾时间序列
Inference for Non-Stationary Heavy Tailed Time Series
论文作者
论文摘要
我们考虑了具有重尾误差分布的非平稳时间序列的推断问题。在一个随时间变化的线性过程框架下，我们表明，通过带有重尾的固定过程存在合适的局部近似值。这使我们能够引入一个基于局部近似值的估计器，该估计器始终如一地估计手头模型的时变参数。为了开发一种健壮的方法，我们还建议一种自我提高的方案，该方案被证明可以恢复估计器的渐近态性，而不管是否存在基本过程的有限差异。提供了有利于这种方法的经验证据。
We consider the problem of inference for non-stationary time series with heavy-tailed error distribution. Under a time-varying linear process framework we show that there exists a suitable local approximation by a stationary process with heavy-tails. This enable us to introduce a local approximation-based estimator which estimates consistently time-varying parameters of the model at hand. To develop a robust method, we also suggest a self-weighing scheme which is shown to recover the asymptotic normality of the estimator regardless of whether the finite variance of the underlying process exists. Empirical evidence favoring this approach is provided.
