Data filtering: from seasonal adjustment to pattern distributions

Thomas M. Menino Convention & Exhibition Center 

In this talk, we first consider David Findley's excellent work on seasonal adjustment of time series. The focus will be on a 2006 paper that considered frequency domain properties of finite (length 49 and 109) concurrent and symmetric SEATS and X-11/12-ARIMA filters for monthly time series. Two main conclusions of the paper were that the squared gains of infinite ARIMA model-based filters are not reliable diagnostics for series of the lengths given above, and the squared gains and phase delays of the concurrent seasonal adjustment filters provide information that is different from (and more valuable for real-time analysis than) that provided by the squared gains of symmetric filters. Beyond that, the work illustrates Dr. Findley's commitment to excellence in sharing informative and useful research.

In the latter part of the talk, we look at data filtering in a very different sense: that of efficiently filtering through Markovian time series to compute distributions of pattern statistics using an auxiliary Markov chain with minimal state spaces.