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Variable screening in high-dimensional vector autoregressions

Author

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  • Ling, Bo
  • Tu, Yundong

Abstract

This paper considers variable screening in the high-dimensional vector autoregressive model. The forward regression screening is used together with an extended Bayesian information criterion to achieve variable screening consistency. To achieve variable selection consistency, a backward elimination algorithm equipped with a modified Bayesian information criterion is further implemented. The asymptotic theoretical results are established under a set of regularity conditions. Finally, numerical results demonstrate the nice finite sample performance of the proposed variable selection procedure.

Suggested Citation

  • Ling, Bo & Tu, Yundong, 2025. "Variable screening in high-dimensional vector autoregressions," Economics Letters, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:ecolet:v:257:y:2025:i:c:s0165176525005324
    DOI: 10.1016/j.econlet.2025.112695
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    References listed on IDEAS

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    1. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    2. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.
    3. Jianqing Fan & Yunbei Ma & Wei Dai, 2014. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1270-1284, September.
    4. Wang, Hansheng, 2009. "Forward Regression for Ultra-High Dimensional Variable Screening," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1512-1524.
    5. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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