Indeed. Especially for forecasting purposes! (multiple equation models employing lagged endogenous variables exhibiting autocorrelation and all that)

]]>not performing a DW statistic on a regression relating highly autocorrelated series would be inconceivable for any econometrician after 1974

Any reasonably knowledgable econometrician would perform such tests on ALL the time series in use PRIOR to any regression, in order to determine the existance of units roots and hence understand the nature of the data being analysed and the potential statistical pitfalls that could result.

That has always been one of the glaring omissions on all hte time series work on temperature or proxies – some basic unit root test on the data and a description of whether they are stationary or not.

]]>The connection to the term “spurious” is building across these notes, but already a key point is worth stressing. When you do a regression the package mechanically computes the ratio of the estimated parameter to the estimated standard error and sticks it in a column under the heading “t-statistic”. But that is no guarantee the number therein came from a data generating process that follows a t-distribution. You have to be able to rule out some influential model misspecification problems. Otherwise you might be comparing your “t-statistic” to the wrong critical values. In the case of Granger and Newbold they looked at regressing random walks on each other. In that case a “t-stat” of, say, 4.0 does not mean the relationship is significant since the ratio in question doesn’t follow a t distribution. “Spurious significance” in this sense means comparing your test statistic to the wrong benchmark and concluding you have significance when in reality you do not. ]]>