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 R Programming Sample Assignment

Implement reduced rank regression as a simple R function.
The R code is displayed below.
ReducedRankRegression <- function(Xraw,Y,Rank) {
# This code implements a two-stage reduced-rank estimation.
# First, we estimate the covariance matrix Sigma of the residuals
# via OLS. Second, we engage reduced-rank regression supplying Sigma
# as an input.
Xraw <- as.matrix(Xraw);
Y <- as.matrix(Y);
N <- dim(Y)[1];
q <- dim(Y)[2];
X <- cbind(rep(1,N), Xraw);
OlsRank <- dim(X)[2];
EffectiveRank <- Rank + 1;
Bols <- t( solve( t(X)%*%X ) %*% t(X)%*%Y );
Sigma <- matrix(0,q,q);
Sxx <- matrix(0,q+1,q+1);
Sxy <- matrix(0,q+1,q);
Syy <- matrix(0,q,q);
for(t in 1:N) {
Sxx <- Sxx + X[t,]%*%t(X[t,]);
Sxy <- Sxy + X[t,]%*%t(Y[t,]);
Syy <- Syy + Y[t,]%*%t(Y[t,]);
Resid <- Y[t,] - Bols%*%X[t,];
Sigma <- Sigma + Resid%*%t(Resid);
Sxx <- Sxx / N;
Sxy <- Sxy / N;
Syx <- t(Sxy);
Syy <- Syy / N;
Sigma <- Sigma / (N-OlsRank);
Gamma <- solve(Sigma);
HalfGamma <- chol(Gamma);
H <- HalfGamma%*%Syx%*%solve(Sxx)%*%Sxy%*%HalfGamma;
SVDoutput <- svd(H);
RelevEigenVectors <- SVDoutput$u[,1:EffectiveRank];
MiddleSum <- matrix(0,q,q);
for(j in 1:EffectiveRank) {
MiddleSum <- MiddleSum +
} B <- solve(HalfGamma)%*%MiddleSum%*%HalfGamma %*%
list(B=B, Sigma=Sigma, Bols=Bols);