So, what is a good value for R-squared? It depends on how you measure it! If you
measure it as a percentage of the variance of the "original" (e.g., deflated but
otherwise untransformed) series, then a simple time series model may achieve an Rsquared
above 90%. On the other hand, if you measure R-squared as a percentage of a
properly stationarized series, then an R-squared of 25% may be quite respectable. (In
fact, an R-squared of 10% or even as little as 5% may be statistically significant in
some applications, such as predicting stock returns.) If you calculate R-squared as a
percentage of the variance in the errors of the best time series model that can be
explained by adding exogenous regressors, you may be disillusioned at how small this
percentage is! Here it was less than 4%, although this was technically a "statistically
significant" reduction, since the coefficients of the additional regressors were
significantly different from zero.