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Sparse seasonal and periodic vector autoregressive modeling

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  • Baek, Changryong
  • Davis, Richard A.
  • Pipiras, Vladas

Abstract

Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies for sparse modeling are presented in this work: first, based on regularization involving adaptive lasso and, second, extending the approach of Davis et al. (2015) for vector autoregressions based on partial spectral coherences. The methods are shown to work well on simulated data, and to perform well on several examples of real vector time series exhibiting cyclical variations.

Suggested Citation

  • Baek, Changryong & Davis, Richard A. & Pipiras, Vladas, 2017. "Sparse seasonal and periodic vector autoregressive modeling," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 103-126.
    • Handle: RePEc:eee:csdana:v:106:y:2017:i:c:p:103-126
    DOI: 10.1016/j.csda.2016.09.005
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    Cited by:

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    2. Bouchouia, Mohammed & Portier, François, 2021. "High dimensional regression for regenerative time-series: An application to road traffic modeling," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    3. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

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