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Simultaneous confidence bands for time-series prediction function

Author

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  • Li Wang
  • Lijian Yang

Abstract

Although many types of confidence bands exist for nonparametric regression with i.i.d. data, theoretical properties of such bands have never been established under dependence. We propose simultaneous confidence bands for nonparametric prediction function of time-series data using spline estimation. Asymptotic properties are established under the assumption of strong mixing, and simulation experiments have provided strong evidence that corroborates with the asymptotic theory. As an application, after removing the environmental Kuznets curve trend effects, the impact of the economic intervention on environmental quality change is quantified for the USA and Japan, with different conclusions.

Suggested Citation

  • Li Wang & Lijian Yang, 2010. "Simultaneous confidence bands for time-series prediction function," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(8), pages 999-1018.
    • Handle: RePEc:taf:gnstxx:v:22:y:2010:i:8:p:999-1018
    DOI: 10.1080/10485251003592575
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    Cited by:

    1. Yujiao Yang & Qiongxia Song, 2014. "Jump detection in time series nonparametric regression models: a polynomial spline approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 325-344, April.
    2. Yujiao Yang & Yuhang Xu & Qiongxia Song, 2012. "Spline confidence bands for variance functions in nonparametric time series regressive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 699-714.
    3. L. Tang & Q. Shao, 2014. "Efficient Estimation For Periodic Autoregressive Coefficients Via Residuals," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 378-389, July.

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