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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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    1. Kilian, Lutz & Demiroglu, Ufuk, 2000. "Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 40-50, January.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, vol. 132(2), pages 337-362, June.
    4. Penm, Jack H W & Penm, Jammie H & Terrell, R D, 1992. "Using the Bootstrap as an Aid in Choosing the Approximate Representation for Vector Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 213-219, April.
    5. Hafer, R. W. & Sheehan, Richard G., 1989. "The sensitivity of VAR forecasts to alternative lag structures," International Journal of Forecasting, Elsevier, vol. 5(3), pages 399-408.
    6. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, Oxford University Press, vol. 113(3), pages 869-902.
    7. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    8. Wang, Hansheng & Leng, Chenlei, 2008. "A note on adaptive group lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5277-5286, August.
    9. 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.
    10. Ren, Yunwen & Zhang, Xinsheng, 2010. "Subset selection for vector autoregressive processes via adaptive Lasso," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1705-1712, December.
    11. Braun, Phillip A. & Mittnik, Stefan, 1993. "Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions," Journal of Econometrics, Elsevier, vol. 59(3), pages 319-341, October.
    12. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    13. Ivanov Ventzislav & Kilian Lutz, 2005. "A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-36, March.
    14. 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.
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    Cited by:

    1. 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.
    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. repec:eee:csdana:v:124:y:2018:i:c:p:27-52 is not listed on IDEAS

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