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Yoshimasa Uematsu and Shinya Tanaka
 
''Regularization parameter selection via cross-validation in the presence of dependent regressors: a simulation study''
( 2016, Vol. 36 No.1 )
 
 
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
JEL: C4 - Econometric and Statistical Methods: Special Topics
C1 - Econometric and Statistical Methods: General
 
Manuscript Received : Jan 12 2016 Manuscript Accepted : Mar 17 2016

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