Tim Curtin’s paper in TSWJ isn’t the first time he’s mis-applied the Durbin-Watson test in order to justify rejecting a regression of physical variables (he substituted regression of the *differenced* values, after which he found the regression not statistically significant). He also does so in this precursor, in which he explicitly states (regarding his first regression)

… the Durbin-Watson statistic at 1.313, which is well below the benchmark 2.0 …

I don’t see any other interpretation than that he was using two (in fact, two point zero) as his critical test value, which we have already mentioned is completely wrong.

Curtin claimed that the absence of autocorrelation is required for valid regression, which is also wrong. Nonetheless he uses that claim to justify requiring regression be performed on differenced variables. Curtin isn’t the first (and won’t be the last) to claim that regression of climate variables like global temperature should be done using differenced variables. For example, a recent commenter on RealClimate by the handle “t.marvell” did the same, justifying it by insisting that global temperature was not a stationary time series. Of course it’s not stationary — it shows a trend!

Neither of those individuals seems to understand the impact that first-differencing has on regression analysis, *especially* when the causal relationship we’re interested in has to do with the *trends* which are present. Let’s give that some consideration.