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Two-step adaptive model selection for vector autoregressive processes

Author

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  • Ren, Yunwen
  • Xiao, Zhiguo
  • Zhang, Xinsheng

Abstract

Model selection (lag order selection and coefficient matrices substructures determination) is an integral part of statistical analysis of vector autoregression (VAR) models. This paper proposes a two-step shrinkage method for VAR model selection. The proposed method can be implemented through a simple algorithm. The resulting estimator is unbiased and subset-selection consistent, and the estimator of the nonzero components of the true parameter vector has asymptotically normal distribution. Limited finite sample Monte Carlo studies suggest that the proposed method outperforms existing alternatives in terms of accuracy in lag order estimation, forecasting and impulse response analysis. We also apply the proposed method to a multivariate macroeconomic time series for illustration.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:116:y:2013:i:c:p:349-364
    DOI: 10.1016/j.jmva.2013.01.004
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:csdana:v:124:y:2018:i:c:p:27-52 is not listed on IDEAS
    2. Florian Ziel, 2015. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes," Papers 1502.06557, arXiv.org, revised Dec 2015.
    3. Xie, Fang & Xu, Lihu & Yang, Youcai, 2017. "Lasso for sparse linear regression with exponentially β-mixing errors," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 64-70.
    4. Ziel, Florian, 2016. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 773-793.

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