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Regularization parameter selection via cross-validation in the presence of dependent regressors: a simulation study

Author

Listed:
  • Yoshimasa Uematsu

    (The Institute of Statistical Mathematics)

  • Shinya Tanaka

    (Otaru University of Commerce)

Abstract

This letter reveals using simulation studies that regularization parameter selection via cross-validation (CV) in penalized regressions (e.g., Lasso) is valid even if the regressors are weakly dependent. In CV procedure, the time series structure of the data set is broken, meaning that there may occur a fatal problem unless the sample is i.i.d.; the estimation accuracy in the training step could be worse due to corruption of data continuity, which may furthermore lead to a bad choice of the regularization parameter. Even in such a situation, we find that CV works well as long as the sample size grows. These findings encourage us to apply the selection procedure via CV to macroeconomic empirical analyses with dependent regressors.

Suggested Citation

  • Yoshimasa Uematsu & Shinya Tanaka, 2016. "Regularization parameter selection via cross-validation in the presence of dependent regressors: a simulation study," Economics Bulletin, AccessEcon, vol. 36(1), pages 313-319.
    • Handle: RePEc:ebl:ecbull:eb-16-00034
    as

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    References listed on IDEAS

    as
    1. 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.
    2. Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
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    More about this item

    Keywords

    Regularization parameter selection; Cross-validation; Forecasting; Penalized Regression; High-dimensional time series model;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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