I am currently working through understanding the mechanics of OLS estimates and the hat matrix. One thing I have been searching for without luck is how we know that the term $X'X$ is invertible where $X'$ represents the transpose of $X$.

I understand that $X'X$ is a symmetric matrix, but I also know that being symmetric alone does not guarantee nonsingularity.

For reference I am referring to this equation:

$$ H = X(X'X)^{-1}X' $$

Any help with this is greatly appreciated.

Through the helpful answers below and a few other google searches I think I have found an answer to my question (at least for most cases).

When performing OLS. We have organized our data with $n$ observations and $p$ parameters. In almost every case $n > p$. This means that the columns of $X$ must be linearly independent. $X'X$ results in a matrix with $dim(X'X) = p$. This means that $X'X$ must also have columns that are linearly independent. Because $X'X$ is a square matrix (rows equal columns), it must have rows which are linearly dependent as well (i.e. $rank(X'X) = p$ aka "full rank"). A full rank matrix is always invertible.

Please correct me if I am wrong here, but I think the logic follow.

I used these questions as resources:




  • 3
    $\begingroup$ It isn't necessarily nonsingular. For those concerned about the singular case, you have to understand the inverse as a generalized inverse. $\endgroup$ – whuber Sep 7 '18 at 20:53
  • $\begingroup$ Am I understanding you correctly that the inverse of the $X'X$ matrix can be thought of as a generalized inverse in this case? Analogous to $(n)1/n$? That goes against what I thought was a fundamental property of matrices (not all are invertible). $\endgroup$ – samvoit4 Sep 7 '18 at 21:36
  • $\begingroup$ See en.wikipedia.org/wiki/Generalized_inverse. I believe your question may have answers at stats.stackexchange.com/questions/63143 and stats.stackexchange.com/questions/84036. $\endgroup$ – whuber Sep 7 '18 at 21:40
  • $\begingroup$ To answer the title question, all you need to do is to calculate the determinant of the matrix. If the determinant is zero, it is singular; if not, it is non-singular. $\endgroup$ – Ben Sep 7 '18 at 23:57
  • 3
    $\begingroup$ That conclusion does not follow. The Normal equations can still be solved even when the determinant of $X^{\prime} X$ is zero. That $X^\prime X$ can be singular is evident: simply repeat one column in $X$, for instance. This sort of thing happens so often that all general-purpose OLS software will automatically handle it (typically by dropping the smallest number of columns needed to make the design matrix of full rank). $\endgroup$ – whuber Sep 8 '18 at 15:56

It's a property of the $\text{rank}$ operator when its used on real matrices $\mathbf{A}$: $$ \text{rank}(\mathbf{A}) = \text{rank}(\mathbf{A}') = \text{rank}(\mathbf{A}'\mathbf{A}) = \text{rank}(\mathbf{A}\mathbf{A}'). $$

In your case, the data matrix $\mathbf{X} \in \mathbb{R}^{n \times p}$ is usually tall and skinny ($n > p$), so the rank of everything is the number of linearly independent columns/predictors/covariates/independent variables. If everything is linearly independent $\text{rank}(\mathbf{X}) = p$, and so you have $\mathbf{X}'\mathbf{X}$ is invertible. If you have collinearity, or columns that can be written as linear combinations of others, then $\text{rank}(\mathbf{X}) < p$, and you cannot find a unique inverse for $\mathbf{X}'\mathbf{X}$ (you can, however, find generalized inverses for it).


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