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Empirical likelihood for nonparametric regression functions under $$\rho $$ ρ -mixing high-frequency data

Author

Listed:
  • Wenjing Tang

    (Guangxi Normal University)

  • Yongsong Qin

    (Guangxi Normal University)

Abstract

The wide application of high-frequency data has attracted the in-depth research of scholars in various fields, especially in econometrics and statistics. In this article, we construct a blockwise empirical likelihood (EL) ratio statistic for a nonparametric regression function under $$\rho $$ ρ -mixing high-frequency data and show that the blockwise EL ratio statistic is asymptotically $$\chi ^2$$ χ 2 -type distributed. The asymptotic confidence interval (CI) for the nonparametric regression function based on the blockwise EL approach is thus given. The results of a simulation study on the finite sample performance of the CIs are presented. At the same time the theoretical findings are applied to a real data analysis. Numerical simulation results show that the CIs constructed by the blockwise EL method perform better than those constructed by the normal approximation method.

Suggested Citation

  • Wenjing Tang & Yongsong Qin, 2025. "Empirical likelihood for nonparametric regression functions under $$\rho $$ ρ -mixing high-frequency data," Statistical Papers, Springer, vol. 66(3), pages 1-27, April.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:3:d:10.1007_s00362-025-01683-0
    DOI: 10.1007/s00362-025-01683-0
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    References listed on IDEAS

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    1. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2010. "Nonlinearity and temporal dependence," Journal of Econometrics, Elsevier, vol. 155(2), pages 155-169, April.
    2. Yongsong Qin & Yinghua Li & Weizhen Yang & Qingzhu Lei, 2011. "Confidence intervals for nonparametric regression functions under negatively associated errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 645-659.
    3. Bradley, Richard C. & Bryc, Wlodzimierz, 1985. "Multilinear forms and measures of dependence between random variables," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 335-367, June.
    4. Lorenzo Camponovo & Yukitoshi Matsushita & Taisuke Otsu, 2020. "Empirical likelihood for high frequency data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 621-632, July.
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