Detecting changes in mean in the presence of time-varying autocovariance
Detecting changes in mean in the presence of time-varying autocovariance
复制标题
检测存在时变自协方差的均值变化
作者：
McGonigle E
近年来，对分段常数时间序列均值变化的检测问题引起了人们的广泛关注。通常，方法假设噪声可以被认为是独立的，同分布的（IID），这在实践中可能不是一个合理的假设。对于具有非平凡自协方差结构的时间序列均值变点检测问题的研究相对较少。在本文中，我们提出了一种基于似然的方法，使用小波来检测表现为时变自协方差的时间序列中的平均值变化。通过模拟研究表明，我们提出的技术可以很好地处理具有各种误差结构的时间序列，并且我们在经济学中出现的两个数据示例上证明了其有效性。
There has been much attention in recent years to the problem of detecting mean changes in a piecewise constant time series. Often, methods assume that the noise can be taken to be independent, identically distributed (IID), which in practice may not be a reasonable assumption. There is comparatively little work studying the problem of mean changepoint detection in time series with nontrivial autocovariance structure. In this article, we propose a likelihood‐based method using wavelets to detect changes in mean in time series that exhibit time‐varying autocovariance. Our proposed technique is shown to work well for time series with a variety of error structures via a simulation study, and we demonstrate its effectiveness on two data examples arising in economics.