Suppose are three time series, $X_1$, $X_2$ and $Y$
Running ordinary linear regression on $Y$ ~ $X_1$ ($Y = b X_1 + b_0 + \epsilon$ ), we get $R^2 = U$. The ordinary linear regression $Y$ ~ $X_2$ get $R^2 = V$. Assume $U < V$
What's the minimum and maximum possible values of $R^2$ on regression $Y$ ~ $X_1 + X_2$ ($Y = b_1 X_1 + b_2 X_2 + b_0 + \epsilon$ )?
I believe the minimum $R^2$ should be $V$ + a small value, since adding new variables always increases $R^2$, but I don't know how to quantify this small value, and I don't know how to obtain the maximum range.