SummaryThe paper proposes a test for constant correlations that allow for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is because incorrect standardization of the series transfers to the sample correlations onto which the tests are built. Correcting for variance breaks at unknown time will have an asymptotic effect. To discuss adjustments, we tackle the issue more generally by considering partial-sums-based inference on moment properties of unobserved processes that is conducted on the basis of estimated counterparts obtained in a preliminary step. The paper gives a characterization of the conditions under which the effect of filtering does not vanish asymptotically. The analysis extends to models with breaks in parameters at estimated time.

中文翻译：

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在结构变化下测试滤波序列的常数相关性

总结本文提出了一种针对常数相关性的检验，该检验允许在未知时间的边际均值和方差中出现中断。从理论上并且在对美国和德国股票收益的应用中，我们发现不考虑边际矩的变化会带来严重的后果。这是因为序列的不正确的标准化转移到了建立测试的样本相关性。校正未知时间的方差中断将具有渐近效果。为了讨论调整，我们通过考虑基于未和过程的矩特性的基于部分和的推断来更广泛地解决该问题，该推断基于在初步步骤中获得的估计对等物进行。本文给出了在不渐近消失的情况下滤波效果的特征。