In vectorized OLS, let $\mathbf{y}$ and $\hat{\mathbf{y}}$ be the observations and predictions, and define the residuals as $\mathbf{e} = \mathbf{y} - \hat{\mathbf{y}}$. Then the uncentered $R^2$ is
$$ R^2_{uc} = 1 - \frac{\mathbf{e}^{\top} \mathbf{e}}{\mathbf{y}^{\top} \mathbf{y}}. $$
Now in his econometrics textbook, Hayashi writes:
Since both $\hat{\mathbf{y}}^{\top} \hat{\mathbf{y}}$ and $\mathbf{e}^{\top} \mathbf{e}$ are nonnegative, $0 \leq R^2_{uc} \leq 1$.
However, this restriction that $R^2_{uc}$ is nonnegative does not make senes to me. $R^2_{uc}$ should be negative when $\mathbf{e}^{\top}\mathbf{e} > \mathbf{y}^{\top}\mathbf{y}$. This is true when
$$ \begin{aligned} \hat{\mathbf{y}} &> 2\mathbf{y} \\ \hat{\mathbf{y}}^{\top} \hat{\mathbf{y}} &> 2\mathbf{y}^{\top} \hat{\mathbf{y}} \\ \mathbf{y}^{\top} \mathbf{y} + \hat{\mathbf{y}}^{\top} \hat{\mathbf{y}} - 2\mathbf{y}^{\top} \hat{\mathbf{y}} &> \mathbf{y}^{\top}\mathbf{y} \\ (\mathbf{y} - \hat{\mathbf{y}})^{\top} (\mathbf{y} - \hat{\mathbf{y}}) &> \mathbf{y}^{\top}\mathbf{y} \\ \mathbf{e}^{\top}\mathbf{e} &> \mathbf{y}^{\top}\mathbf{y} \end{aligned} $$
So why can't $R_{uc}^2$ be negative?