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On constrained estimation of graphical time series models

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  • Yuen, T.P.
  • Wong, H.
  • Yiu, K.F.C.

Abstract

Graphical time series models encode the conditional independence among the variables of a multivariate time series. An iterative method is proposed to estimate a graphical time series model based on a sparse vector autoregressive process. The method estimates both the autoregressive coefficients and the inverse of noise covariance matrix under sparsity constraints on both the coefficients and the inverse covariance matrix. This iterative method estimates a sparse vector autoregressive model by considering maximum likelihood estimation with the sparsity constraints as a biconcave problem, where the optimization problem becomes concave when either the autoregressive coefficients or the inverse noise covariance matrix is fixed. The method also imposes fewer restrictions in the estimation comparing to the use of a structural vector autoregressive model to study the dynamic interdependencies between time series variables.

Suggested Citation

  • Yuen, T.P. & Wong, H. & Yiu, K.F.C., 2018. "On constrained estimation of graphical time series models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 27-52.
    • Handle: RePEc:eee:csdana:v:124:y:2018:i:c:p:27-52
    DOI: 10.1016/j.csda.2018.01.019
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    References listed on IDEAS

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    1. Jochen Gorski & Frank Pfeuffer & Kathrin Klamroth, 2007. "Biconvex sets and optimization with biconvex functions: a survey and extensions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(3), pages 373-407, December.
    2. Hsu, Nan-Jung & Hung, Hung-Lin & Chang, Ya-Mei, 2008. "Subset selection for vector autoregressive processes using Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3645-3657, March.
    3. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    4. Ren, Yunwen & Xiao, Zhiguo & Zhang, Xinsheng, 2013. "Two-step adaptive model selection for vector autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 349-364.
    5. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    6. Brillinger, David R., 1996. "Remarks Concerning Graphical Models for Time Series and Point Processes," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 16(1), November.
    7. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    8. Fei Hu & Zudi Lu & Heung Wong & Tsz P. Yuen, 2016. "Analysis of air quality time series of Hong Kong with graphical modeling," Environmetrics, John Wiley & Sons, Ltd., vol. 27(3), pages 169-181, May.
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    Cited by:

    1. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Zeda Li & William W. S. Wei, 2024. "Measuring the advantages of contemporaneous aggregation in forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1308-1320, August.
    3. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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