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Seasonality robust local whittle estimation

机译:季节性强大的本地削减估计

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摘要

Time series that have seasonal effects with long periods relative to the observation frequency can exhibit spurious long memory. The effect of these seasonalities on the periodogram is similar to that of structural breaks and non-periodic trends, but it only influences the seasonal frequencies and their harmonics. Still, the effect causes a sizable bias of popular estimators such as the local Whittle estimator. To overcome this, we propose a robust local Whittle estimator based on the omission of the affected periodogram ordinates. In a Monte Carlo study, we compare this estimator with a robust log-periodogram regression-based estimator known in the literature. An application to electricity load series demonstrates the potential of robust estimators for empirical research.
机译:具有相对于观察频率长时间的季节性效果的时间序列可以表现出杂散的长记忆。这些季节性对周期图的影响与结构性休息和非周期性趋势类似,但它只影响季节频率及其谐波。尽管如此,效果导致诸如本地Whittle估计器的流行估计器的相当大的偏差。为了克服这一点,我们提出了一种基于受影响期间句柄的遗漏的强大的本地Whittle估计。在蒙特卡罗研究中,我们将该估算器与文献中已知的基于鲁棒的日志周期图回归的估计进行了比较。电力负荷系列的应用展示了实证研究稳健估计的潜力。

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