From a statistical perspective, some authors have found R^{data}_t (as used in the observation equation guide paper) to be non-stationary statistically.

Nevertheless, R^{data}_t is used in some reduced-form models (including Bernanke’s FAVAR model) with no transformation, implying R^{data}_t is stationary in levels. I guess the support for stationarity, in this case, would be the following statements from the “A guide to specifying observations equations…” paper:

“For nominal interest rates the evidence is not as clear-cut, but at least for most developed economies where the (Generalized) Taylor principle should have been satisfied, they should also be stationary”

Thus, stationarity could be supported by a statistical test, the Taylor principle (for developed economies), and perhaps by observation? By observation, I mean can we say a non-trending series is stationary and can be used in a model although it fails a statistical test - say ADF test?