I want to calculate the variance-covariance matrix of a single variable:
$$
\begin{align*}
Var({\bf y}) & = E({\bf yy'}) – E({\bf y})E({\bf y'}) \\
& = \left( \begin{array}{cccc}
\sigma_{y_{1}}^{2} & \sigma_{y_{1}y_{2}} & \cdots & \sigma_{y_{1}y_{n}} \\
\sigma_{y_{1}y_{2}} & \sigma_{y_{2}}^{2} & \cdots & \sigma_{y_{2}y_{n}} \\
\vdots & \vdots & \ddots & \vdots \\
\sigma_{y_{1}y_{n}} & \sigma_{y_{2}y_{n}} & \cdots & \sigma_{y_{n}}^{2}
\end{array} \right)\\
\end{align*}$$
for a single variable ${\bf y}$:
$$
\begin{align*}
{\bf y} = \left( \begin{matrix}
y_1 \\ y_2 \\ \vdots \\ y_n \end{matrix} \right)
\end{align*}
$$
but I do not understand how to calculate $\sigma_{y_{1}y_{2}}$ since $y_{1}$ and $y_{2}$ are just two numbers.
Any help?
Update:
Take for example the residuals of an OLS:
$$ \begin{align*}
{\bf e} &= ({\bf I} – {\bf X} ({\bf X}^\prime {\bf X})^{-1} {\bf X}^\prime) {\bf y}\\
&= ({\bf I} – {\bf H}) {\bf y}
\end{align*}
$$
For 10 observations, ${\bf e}$ is a $10 \times 1$ matrix. And ${\bf e} = (e_1 \quad e_2 \quad \ldots \quad e_{10})^\prime$.
$e_1$ is just a number, like $e_2$. How do I calculate the second term in first row of the variance-covariance matrix of the residuals, $Cov(e_1, e_2)$?
The residual variance-covariance matrix is $({\bf I} – {\bf H}){\bf \Omega}({\bf I} – {\bf H})^{\prime}$, but I would like to learn a more "manual" way if possible.
Best Answer
You're confusing the index of a variable with an index of an observation. In your first equation, there are no indices of observations: $y_1$ refers to the first variable of the vector of variables $y_i$. Hence, you have $\sigma_{y_1y_2}$ refers to the covariance of the variables $y_1,y_2$.
In your update, the variable $e$ is just one variable, and $e_1,e_2,\dots$ refer to the observations of this variable. So, $e_1$ is the first observation of the single variable $e$, it's not the first variable in the vector of variables. Hence, the your first equation does not apply to this case. In fact, the only thing that's related to this case is the variance $\sigma^2_{y_1}$ - the variance of the variable $y_1$.
To compute the variance of a variable $e$ you don't use the first equation, you use other relevant equations. For instance the standard estmator of the sample variance $s^2=\frac{1}{n-1}\sum_{i=1}^n(e_i-\bar e)^2$