Show that $\hat{\beta}_0 = \bar{y}$ for OLS when the columns of $\mathbf{X}$ are centered Let's assume the general linear model $\mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\epsilon$, where $\mathbf{y} \in \mathbb{R}^N$, $\mathbf{X}$ is a $N \times (p+1)$ matrix (where $p+1 < N$) with all entries in $\mathbb{R}$, $\boldsymbol\beta \in \mathbb{R}^{p+1}$, and $\boldsymbol\epsilon$ is a $N$-dimensional vector of real-valued random variables with $\mathbb{E}[\boldsymbol\epsilon] = \mathbf{0}_{N \times 1}$.
In the development of ridge regression, Introduction to Statistical Learning (p. 215) and Elements of Statistical Learning (p. 64) mention that $\beta_0$ is estimated using $\bar{y} = \dfrac{1}{N}\sum_{i=1}^{N}y_i$ after centering the $\mathbf{X}$ columns, and then each component of $\mathbf{y}$ is centered using $\bar{y}$ prior to performing ridge regression.
Under OLS estimation, $$\hat{\boldsymbol\beta}_{\mathbf{X}} = (\mathbf{X}^{T}\mathbf{X})^{-1}\mathbf{X}^{T}\mathbf{y}\text{.}$$
It can be shown that the matrix
$$\tilde{\mathbf{X}} = \left(\mathbf{I}_{N \times N}-\dfrac{1}{N}\mathbf{1}_{N \times N}\right)\mathbf{X}$$
centers the columns of $\mathbf{X}$, where $\mathbf{1}_{N \times N}$ is the  $N \times N$ matrix of all $1$s, and $\mathbf{I}_{N \times N}$ is the $N \times N$ identity matrix.
I am interested in showing that $\hat{\beta}_0$ - i.e., the first component of $\hat{\boldsymbol\beta}$ - is equal to $\bar{y}$ using these assumptions. I thought maybe  a previous question would help, but this deals with the case when $\mathbf{X}$ is right-multiplied, rather than left-multiplied. 
Using the above,
$$\tilde{\mathbf{X}}^{T}\tilde{\mathbf{X}} = \mathbf{X}^{T} \left(\mathbf{I}_{N \times N}-\dfrac{1}{N}\mathbf{1}_{N \times N}\right)\mathbf{X}$$
due to that the matrix $\mathbf{I}_{N \times N}-\dfrac{1}{N}\mathbf{1}_{N \times N}$ is symmetric and idempotent. 
Let's suppose that $$\mathbf{X} = \begin{bmatrix}
\mathbf{1}_{N \times 1} & \mathbf{x}_1 & \cdots & \mathbf{x}_p
\end{bmatrix}$$
so that $$\mathbf{X}^{T} = \begin{bmatrix}
\mathbf{1}_{N \times 1}^{T} \\
 \mathbf{x}_1^{T} \\
\vdots \\
 \mathbf{x}_p^{T}
\end{bmatrix}\text{.}$$
We also have
$$\mathbf{I}_{N \times N}-\dfrac{1}{N}\mathbf{1}_{N \times N} = \begin{bmatrix}
1-\frac{1}{N} & -\frac{1}{N} & \cdots & -\frac{1}{N} \\
-\frac{1}{N} & 1 - \frac{1}{N} & \ddots & -\frac{1}{N} \\
\vdots & \ddots & \ddots & -\frac{1}{N} \\
-\frac{1}{N} & \cdots & -\frac{1}{N} & 1 - \frac{1}{N}
\end{bmatrix} $$
As I started thinking about doing the multiplication and calculating an inverse, I think I'm at a dead end here. Any suggestions?
 A: By assumption, your design matrix $X$ can be partitioned as 
$$X = \begin{bmatrix}
1 & \tilde{X}
\end{bmatrix},$$
where $\tilde{X} \in \mathbb{R}^{N \times p}$ satisfies $1^T\tilde{X} = \mathbf{0}^T$ due to the centering. Partition $\beta$ into $[\beta_0, \tilde{\beta}^T]^T$ accordingly. Straightforward calculation shows
\begin{align*}
\hat{\beta} & = \begin{bmatrix}\hat{\beta}_0 \\ \hat{\tilde{\beta}}\end{bmatrix} \\ 
& = (X^TX)^{-1}X^Ty \\
& = \begin{bmatrix}
1^T1 & 1^T\tilde{X} \\
\tilde{X}^T1 & \tilde{X}^T\tilde{X}
\end{bmatrix}^{-1} \begin{bmatrix}1^T \\ \tilde{X}^T\end{bmatrix} y \\
& = \begin{bmatrix}N^{-1} & 0^T \\ 0 & (\tilde{X}^T\tilde{X})^{-1}\end{bmatrix}
\begin{bmatrix}1^Ty \\ \tilde{X}^Ty\end{bmatrix} \\
& = \begin{bmatrix} \bar{y} \\ (\tilde{X}^T\tilde{X})^{-1}\tilde{X}^Ty \end{bmatrix}.
\end{align*}
A: Let $\tilde{X}$ be a $N \times p$ matrix with $N$ observations and $p$ features:
$$\mathbf{X} = (1 \quad\tilde{X})= \begin{bmatrix}
1 &x_{11} & x_{12} & \cdots & x_{1p} \\
1 &x_{21} & x_{22} & \ddots & x_{2p} \\
\vdots &\vdots & \ddots & \ddots & x_{N-1,p} \\
1 &x_{N1} & \cdots & x_{N,p-1} & x_{Np}
\end{bmatrix} $$
Given that columns of $\tilde{X}$ are centred, we have:
$$\sum_{i=1}^{N}x_{ij} = 0  \quad \forall \:j\in {1,...p} $$
$$(\mathbf{X}^T\mathbf{X})= \begin{bmatrix}
N &0^T \\
0 & \Sigma\end{bmatrix}$$
where $\Sigma$ is a matrix of pairwise covariances between columns of $\tilde{X}$
$$\hat{\boldsymbol\beta}=(\mathbf{X}^T\mathbf{X})^{-1}(\mathbf{X}^TY) = \begin{bmatrix}
\frac{1}{N} &0^T \\
0 & \Sigma^{-1}\end{bmatrix}\begin{bmatrix}N\overline{Y} \\ \vdots\end{bmatrix} $$
$$\hat{\boldsymbol\beta}_0=\frac{1}{N}N\overline{Y}=\overline{Y} $$
