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Subset selection for vector autoregressive processes using Lasso

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  • Hsu, Nan-Jung
  • Hung, Hung-Lin
  • Chang, Ya-Mei

Abstract

A subset selection method is proposed for vector autoregressive (VAR) processes using the Lasso [Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B 58, 267-288] technique. Simply speaking, Lasso is a shrinkage method in a regression setup which selects the model and estimates the parameters simultaneously. Compared to the conventional information-based methods such as AIC and BIC, the Lasso approach avoids computationally intensive and exhaustive search. On the other hand, compared to the existing subset selection methods with parameter constraints such as the top-down and bottom-up strategies, the Lasso method is computationally efficient and its result is robust to the order of series included in the autoregressive model. We derive the asymptotic theorem for the Lasso estimator under VAR processes. Simulation results demonstrate that the Lasso method performs better than several conventional subset selection methods for small samples in terms of prediction mean squared errors and estimation errors under various settings. The methodology is applied to modeling U.S. macroeconomic data for illustration.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3645-3657
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    References listed on IDEAS

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    1. Jack H. W. Penm & R. D. Terrell, 1982. "On The Recursive Fitting Of Subset Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(1), pages 43-59, 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. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, 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. Hansheng Wang & Guodong Li & Chih‐Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78, February.
    6. Penm, J. H. W. & Terrell, R. D., 1984. "Multivariate subset autoregressive modelling with zero constraints for detecting 'overall causality'," Journal of Econometrics, Elsevier, vol. 24(3), pages 311-330, March.
    7. Chen, Changhua & Davis, Richard A. & Brockwell, Peter J., 1996. "Order Determination for Multivariate Autoregressive Processes Using Resampling Methods," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 175-190, May.
    8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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