An interesting tangential argument I had with a colleague recently was how to actually calculate R^2 when testing model predictions on new data (emphasis on new

An interesting tangential argument I had with a colleague recently was how to actually calculate R^2 when testing model predictions on new data (emphasis on new data). He claimed the conventional way is as the square of the correlation coefficient of the two lists of data, whereas I was suggesting 1 - SSE/SST in accord with references I read (well, Wikipedia), which latter method yields lower values for his data. I know they are equal on training data when using linear regression with constant term, but generally they differ. Correlation way being scale and shift invariant to me disqualifies it as a measure for predicting new data. Unfortunately he was able to barrage me with many online references that just say that "R^2 is the square of the correlation coefficient", which without very careful reading of the context (fitting not predicting), and sometimes even with such careful reading, makes his interpretation look correct. I find the whole thing rather exasperating... It also occurs to me that just as a question of convention, that I may be wrong which wouldn't surprise me as I'm new to modeling. So: Do most modelers report correlation^2 as their R^2 values for holdout tests? I wonder have other modelers here encountered this confusion?