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Partial autocorrelation parameterization for subset autoregression

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  • A. I. McLeod
  • Y. Zhang

Abstract

. A new version of the partial autocorrelation plot and a new family of subset autoregressive models are introduced. A comprehensive approach to model identification, estimation and diagnostic checking is developed for these models. These models are better suited to efficient model building of high‐order autoregressions with long time series. Several illustrative examples are given.

Suggested Citation

  • A. I. McLeod & Y. Zhang, 2006. "Partial autocorrelation parameterization for subset autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 599-612, July.
    • Handle: RePEc:bla:jtsera:v:27:y:2006:i:4:p:599-612
    DOI: 10.1111/j.1467-9892.2006.00481.x
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    Cited by:

    1. Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila, 2021. "A new BISARMA time series model for forecasting mortality using weather and particulate matter data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 346-364, March.
    2. Sigrunn H. Sørbye & Pedro G. Nicolau & Håvard Rue, 2022. "Finite-sample properties of estimators for first and second order autoregressive processes," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 577-598, October.
    3. Zhihao Xu & Clifford M. Hurvich, 2021. "A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation," Papers 2108.06093, arXiv.org, revised Jun 2023.
    4. Daniel F. Schmidt & Enes Makalic, 2013. "Estimation of stationary autoregressive models with the Bayesian LASSO," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 517-531, September.
    5. McLeod, A. Ian & Zhang, Ying, 2008. "Improved Subset Autoregression: With R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i02).

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