A way to think about principal component analysis is as a matrix approximation. We have a matrix and we want to get a ‘smaller’ matrix with . We want the new ‘smaller’ matrix to be close to the original despite its reduced dimension. Sometimes we say ‘such that Z capture the bulk of comovement in … Continue reading PCA as regression