Adjusted-range-based self-normalized autocorrelation tests
Published in Economics Letters, 2025
Abstract
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.
Recommended citation: Sun, J., Zhu, M., & Linton, O. (2025). "Adjusted-range-based self-normalized autocorrelation tests." Economics Letters, 251, 112315.
Download Paper | Download Bibtex
