Your approach is generally correct but the devil is in the details. Unwarranted differencing can lead to very spurious results. Ignoring anomalies (pulses/seasonal pulses,level shifts and local time trends can be the downfall of a tf model. Changes in parameters over time or changes in error variance over time need to be investigated. I am afraid that both SAS and R are not up to these challenges. You might post your data or send it to me at my email address and I will try and answer your specific questions and post an analysis to the web. If your data is confidential then we can do this off line although I would prefer to enlighten others as to what can be done.

In closing ... it is generally speaking not a good idea to use percentages but rather use both observed series when constructing a model. Care shoiuld be taken to distinguish between trend and level shift acrivity. Some software (JMP in particular) assume that both the parameters and the error variance are constant over time. B wary of such simplistic solutions as they are often wrong and need remedial treatment.