Multivariate time series

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,
Wednesday, February 21, 2018 - 3:20pm - 4:00pm
Christophe Croux (EDHEC Business School)
The Vector AutoRegressive (VAR) Model is a popular model for the analysis of a multivariate time series. It allows to investigate the impact changes in one time series have on other ones. A drawback of the VAR is the risk of overparametrization because the number of parameters increases quadratically with the number of included time series. This undermines the ability to identify important relationships in the data and to make accurate forecasts. In high dimensions, we therefore use sparse estimation.
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