Might be a stupid question, but...
I have two univariate time series models, both explaining variable Y, one with variable X and one with variable Z as the explanatory variable (plus a constant). Now, both models yield an R-squared that is rather close to the other. Can I really say that model X is better than model Z just by comparing these R-squareds (since with 5 observation more or less, things might look different)? Or can I test whether these r-squareds are statistically different from each other? Any other idea to evaluate goodness of fit in that case, except for comparing RMSE? Or is in this case comparing (f-testing) the coefficients of X and Z helpful?