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.
Wednesday, April 25, 2018 - 9:00am - 9:30am
Alain Hecq (Universiteit Maastricht (Rijksuniversiteit Limburg))
This paper aims at evaluating the forecasting performances of a set of univariate fractional white noise processes versus multivariate factor models for realized volatility measures. The literature on the sources of long-memory is quite large, from the aggregation across heterogeneous series to the impact of structural changes that spuriously lead to the detection of fractional integrated process. Alternatively, Chevillon, Hecq and Laurent (2018) investigate the mechanisms underlying the long-memory feature generated from large vector autoregressive models.
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,
Thursday, February 22, 2018 - 11:00am - 12:00pm
Sven Serneels (BASF Corporation)
This lecture will be set up as a panel discussion about the role of forecasting in a corporate big data analytics environment. As a basis for discussion, Sven Serneels will present how the Advanced Business Analytics group is set up at BASF. To create context, selected aspects from BASF's Corporate Overview will be presented, which eventually lead up to the organization and responsibilities of the Advanced Business Analytics group. Finally, successful applications in the areas of forecasting and operations research will be introduced.
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