# Deriving the variance-covariance matrix for parameter vector of a linear regression model

I came across this formula in the book Elements of Statistical Learning in Chapter 3.

$$\operatorname{Var}(\hat\beta) = (\mathbf X^T\mathbf X)^{-1}\hat\sigma^2$$

Here, $$\hat\beta$$ is the estimated parameter vector for a linear regression equation satisfying: $$\mathbf y= \mathbf X\mathbf \beta + \epsilon$$

$$\epsilon$$ in turn follows a normal distribution with mean 0 and variance $$\sigma^2$$. $$\hat\beta$$ follows the normal equation, which has been derived previously in the same chapter: $$\hat\beta = (\mathbf X^T \mathbf X)^{-1}\mathbf X^T\mathbf y$$

What I understand as per the text is that the $$\operatorname{Var}(\hat\beta)$$ is the variance-covariance derivation, and for a matrix I can calculate the variance-matrix given values. But I'm getting stuck in this case as $$\hat\beta$$ is expressed as an equation, which when I use to expand $$\hat\beta$$ in my derivation, I end up with an equation that I cannot simplify further.

• it might be helpful if you include your partial derivation. Mar 29 at 19:32

The key to solving this equation is $$\operatorname{Var}(AW) = A\operatorname{Var}(W)A^T$$