基于调整距离的自归一化自相关测试

IF 1.8 4区经济学 Q2 ECONOMICS

Economics Letters Pub Date : 2025-04-10 DOI:10.1016/j.econlet.2025.112315

Jiajing Sun , Meiting Zhu , Oliver Linton

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引用次数: 0

摘要

本文提出了时间序列中自相关函数（ACF）的基于调整距离的自归一化检验方法，这对于理解时间序列的相关性结构和做出可靠的统计推断至关重要。我们的方法提供了改进的性能，特别是在测试一阶ACF的存在时。我们通过模拟验证了这些测试的有效性，并将其应用于分析北京的COVID-19病例数。结果证实了我们的方法的鲁棒性，有望在复杂数据设置中检测时间依赖性方面取得重大进展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adjusted-range-based self-normalized autocorrelation tests

This paper presents adjusted range-based self-normalized tests for the autocorrelation function (ACF) in time series, which is crucial for understanding the dependence structure and making reliable statistical inferences. Our approach offers improved performance, especially when testing for the presence of first-order ACF. We demonstrate the efficacy of these tests through simulations and apply them to analyze COVID-19 case counts in Beijing. The results confirm the robustness of our methods, promising significant advancements in the detection of temporal dependence in complex data settings.

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来源期刊

Economics Letters ECONOMICS-

CiteScore

3.20

自引率

5.00%

发文量

348

审稿时长

30 days

期刊介绍： Many economists today are concerned by the proliferation of journals and the concomitant labyrinth of research to be conquered in order to reach the specific information they require. To combat this tendency, Economics Letters has been conceived and designed outside the realm of the traditional economics journal. As a Letters Journal, it consists of concise communications (letters) that provide a means of rapid and efficient dissemination of new results, models and methods in all fields of economic research.