Campuses:

High-dimensional Time Series

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.
Monday, April 23, 2018 - 1:00pm - 1:30pm
Ines Wilms (Katholieke Universiteit Leuven)
The Vector AutoRegressive Moving Average (VARMA) model is fundamental
to the study of multivariate time series. However, estimation becomes challenging in
even relatively low-dimensional VARMA models. With growing interest in the simultaneous
modeling of large numbers of marginal time series, many authors have abandoned
the VARMA model in favor of the Vector AutoRegressive (VAR) model, which is seen as a
simpler alternative, both in theory and practice, in this high-dimensional context. However,
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