论文标题
非参数趋势曲线的多尺度比较
Multiscale Comparison of Nonparametric Trend Curves
论文作者
论文摘要
我们开发了新的计量经济学方法来比较非参数时间趋势。在许多应用中，从业者对观察到的时间序列是否具有相同的时间趋势感兴趣。此外，他们经常想知道哪些趋势是不同的，在哪些时间间隔不同。我们设计了一个多尺度测试，以正式解决这些问题。具体而言，我们开发了一项测试，该测试允许对哪种时间趋势不同以及在哪里（即，在哪个时间间隔）进行严格的信心陈述。根据我们的多尺度测试，我们进一步开发了一种聚类算法，该算法允许将观察到的时间序列聚集到具有相同趋势的组中。我们为测试和聚类方法得出渐近理论。该理论是通过模拟研究和GDP增长数据和房屋定价数据的两种应用来补充的。
We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in whether the observed time series all have the same time trend. Moreover, they would often like to know which trends are different and in which time intervals they differ. We design a multiscale test to formally approach these questions. Specifically, we develop a test which allows to make rigorous confidence statements about which time trends are different and where (that is, in which time intervals) they differ. Based on our multiscale test, we further develop a clustering algorithm which allows to cluster the observed time series into groups with the same trend. We derive asymptotic theory for our test and clustering methods. The theory is complemented by a simulation study and two applications to GDP growth data and house pricing data.
