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

Yoshimasa Uematsu () and Shinya Tanaka ()
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Yoshimasa Uematsu: The Institute of Statistical Mathematics
Shinya Tanaka: Otaru University of Commerce

Economics Bulletin, 2016, vol. 36, issue 1, 313-319

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.

Keywords: Regularization parameter selection; Cross-validation; Forecasting; Penalized Regression; High-dimensional time series model (search for similar items in EconPapers)
JEL-codes: C1 C4 (search for similar items in EconPapers)
Date: 2016-03-17
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