I am working on a structural vector autoregression that requires imposing constraints on a matrix factorization.
In particular, I have an N-dimensional positive definite matrix $\Sigma$ that I need to factor with some N-dimensional square matrix $R$, such that $RR'=\Sigma$. Moreover, given some N-dimensional square matrix $C$, let $D \equiv CR$ and assume constraints are imposed such that some elements of $D$ are zero. In particular, enough of these constraints are imposed such that $R$ is identified. I believe this requires $n^2-n*(n+1)/2$
Any idea how to recover $R$?