I'm still thinking this through. My impressions were the same as yours (ie the 3-month T-bill broken into 3-time periods was out-of-bounds) for the included NEAS example. I'm thinking of using the CPI data (non-sesonally adjusted) and testing two/three models: no seasonal adjustment, 6-month seasonal adjustment and(maybe if I have time) 12-month seasonal adjustment.
I'm actually finding it pretty easy to walk through how to do an analysis by using the text book, following through starting in chapter 16. Chapter 19 explains in better detail of what to do when putting a model together. Section 19.1 in particular is a very good step-by-step guide on what to do when assembling a model.
I'll try and post when I'm completed and I would be more than happy to help others along in completing their project! 
JR
[NEAS does not say exactly what to do, but this the proper direction. Begin by dividing into eras; examine the interest rates and the first differences.
Jacob: Should we examine six month seasonality or 12 month seasonality?
Rachel: We normally examine 12 month seasonality, unless we have a reason to assume six month seasonality.
Jacob: What if the six month sample autocorrelation is significant?
Rachel: Annual seasonality may cause a six month autocorrelation. Use a 12 month AR parameter, and see if the six month sample autocorrelation is still significant. If it is, compare out-of-sample forecasts for six month seasonality vs 12 month seasonality. This is a potential student project.
Jacob: What might cause six month seasonality?
Rachel: Auto premiums and policies have six month seasonality because of the six month policies. Actuarial exam statistics have six month seasonality because of the semi-annual exam sittings. University statistics have six month seasonality because of their semesters.
Jacob: Should we use the examples in the textbook for the student project?
Rachel: The last five modules in the course cover Chapter 19; these are good examples, but they are more complex, since the authors fit higher order ARIMA models.
Jacob: Should we review the earlier chapters as well?
Rachel: The entire course focuses on constructing and testing ARIMA models. The earlier chapters explain how to charts, plots, correlograms, sample and partial autocorrelation functions, regression analysis, first and second differences, and the various statistical tests to construct and choose a good model.]