Mixed-frequency data

Monday, April 23, 2018 - 1:30pm - 2:00pm
Ruey Tsay (University of Chicago)
In the last few years, an extensive literature has been focused on the ell-1 penalized least squares (Lasso) estimators of high dimensional linear regression when the number of covariates p is considerably larger than the sample size n. However, there is limited attention paid to the properties of the estimators when the errors or/and the covariates are serially dependent. In this study, we investigate the theoretical properties of the Lasso estimators for linear regression with random design under serially dependent and/or non-sub- Gaussian errors and covariates.
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