\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares.
\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares.
There’s lots of linear algebra out there that’s quite useful for statistics, but that I never learned in school or never had cause to study in depth. In the same spirit as my previous post on the Woodbury identity, I thought I would share my notes on another helpful bit of math about matrices.
As in many parts of life, statistics is full of little bits of knowledge that are useful if you happen to know them, but which hardly anybody ever bothers to mention.