can download both papers free of charge (here and here).

Unfortunately, I currently do not have the time to read these papers. I even had to stop in the middle of Pötscher's paper (he is a former professor of mine).

ad real GDP: Forecasting an estimate with an AR model... I couldn't imagine doing anything more useless ;-D. 

Yes, an AR model for real GDP appears too primitive and sounds like a dull idea. However, this should be viewed in the context of the research program, which aims at developing techniques and algorithms for AUTOMATED forecasting.

The following quote from the abstract of second Phillips' paper that you quote seems to be especially interesting to me:

"In some cases, the forecast performance of the parsimonious Bayes models is substantially superior. The results cast some doubts on the value of working with fixed format time series models in empirical research and demonstrate the practical advantages of evolving-format models. The paper makes a new suggestion for modelling interest rates in terms of reciprocals of levels rather than levels (which display more volatility) and shows that the best data-determined model for this transformed series is a martingale."

I wonder how the empirical estimates and performance of popular linear models would change if their equations are reformulated in terms of reciprocals of r(t)... 

I seem to remember that most of the performance of Litterman's Bayesian VARs actually ended up coming from the comparatively long memory they implied and suspect that this might be the case for these automated forecasting systems too. David Hendry always points out that a simple intercept correction applied to an AR model will usually gain you a sizeable proportion of the improvement in forecasting accuracy that you can get from much more complicated models.