Structural Breaks, Outliers, MDL, Some Theory and Google Trends

In this lecture, we will take another look at modeling time series that exhibit certain types of nonstationarity.  Often one encounters time series for which segments look stationary, but the whole ensemble is nonstationary.  On top of this, each segment of the data may be further contaminated by an unknown number of innovational and/or additive outliers; a situation that presents interesting modeling challenges.  We will seek to find the best fitting model in terms of the minimum description length principle.  As this procedure is computationally intense, strategies for accelerating the computations are required.  Numerical results from simulation experiments and real data analyses, some of which come from Google trends, show that our proposed procedure enjoys excellent empirical properties.  In the case of no outliers, there is an underlying theory that establishes consistency of our method.  The theory is based on an interesting application of the functional law of the iterated logarithm.

(This is joint work with Thomas Lee and Gabriel Rodriguez-Yam.)
Reception at 5:00pm in the 3rd floor lounge of Hanes Hall

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