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Subset regression time series and its modeling procedures

Author

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  • Chen, Zhao-Guo
  • Ni, Jun-Yuan

Abstract

Consider the linear regression model y(n) = x1(n)[theta]1 + ... + xk(n)[theta]k + w(n) with w(n) assumed a linear time series, especially an ARMA series. Procedures which use recursions only are suggested to identify the non-zero [theta]k and the order of ARMA or subset ARMA residuals. The consistency of these procedures is proved. The convergence rate of LS estimation of regression parameters under these assumption is also discussed. Simulations show good results.

Suggested Citation

  • Chen, Zhao-Guo & Ni, Jun-Yuan, 1989. "Subset regression time series and its modeling procedures," Journal of Multivariate Analysis, Elsevier, vol. 31(2), pages 266-288, November.
    • Handle: RePEc:eee:jmvana:v:31:y:1989:i:2:p:266-288
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    Citations

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    Cited by:

    1. You, Jinhong & Zhou, Xian & Zhu, Li-Xing, 2009. "Inference on a regression model with noised variables and serially correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1182-1197, July.
    2. Ching-Kang Ing & Ching-Zong Wei, 2005. "A maximal moment inequality for long range dependent time series with applications to estimation and model selection," Econometrics 0508009, University Library of Munich, Germany.
    3. Jinhong You & Xian Zhou & Lixing Zhu & Bin Zhou, 2011. "Weighted denoised minimum distance estimation in a regression model with autocorrelated measurement errors," Statistical Papers, Springer, vol. 52(2), pages 263-286, May.
    4. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.

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