Two widely-used seasonal adjustment programs are the U.S. Census Bureau's X-12-ARIMA and the SEATS program for ARIMA-model-based signal extraction written by Agustin Maravall. In previous studies with SEATS and X-12-ARIMA, we found some series where the adjustment from SEATS had smaller revisions than the adjustment from X-12-ARIMA (Hood, Ashley, and Findley, 2000). Based on this previous work, I will investigate the properties of a time series that make it a good candidate for adjustment by SEATS or by X-12-ARIMA. I used a version of X-12-ARIMA that has access to the SEATS algorithm. This allows computation of similar diagnostics for both programs — including sliding spans and revision diagnostics — to compare adjustments between the two programs.
In our earlier studies, we found that SEATS needs more diagnostics before we can recommend using SEATS for production work at the Bureau. In this paper, I show examples of why the diagnostics in X-12-SEATS are very useful. For example, SEATS can induce residual seasonality into the seasonally adjusted series when the original series isn't seasonal. The spectral diagnostics available in X-12-SEATS are very important to be able to see if the original series is seasonal or not. I also show an example of a series with very large revisions due to the model chosen by TRAMO. The revision history diagnostics are very useful to see series with large revisions.
seasonal adjustment, time series
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