The mvregress function comes at this a little differently, but it is possible to set up the problem using mvregress. For this sample problem:
n = 100; p = 3; d = 4;
x = randn(n,p);
c = rand(p,d)
e = randn(n,d)/10;
y = x*c + e;
I can use backlash to get a matrix of coefficients:
x\y
and I can get the same coefficients from mvregress this way:
for j=1:n; X{j} = kron(eye(d),x(j,:)); end
mvregress(X,y)
This will give you some additional outputs, though not all you may be looking for. For example, the fourth output is the estimated covariance of the coefficient estimates, so you can take the square root of the diagonal values to get standard errors.
