Node:Linear Least Squares, Previous:Nonlinear Programming, Up:Optimization
[beta, v, r] = gls (y, x, o) | Function File |
Generalized least squares estimation for the multivariate model
y = x b + e with mean (e) = 0 and
cov (vec (e)) = (s^2) o,
where
y is a t by p matrix, x is a t by
k matrix, b is a k by p matrix, e
is a t by p matrix, and o is a t p by
t p matrix.
Each row of y and x is an observation and each column a variable. The return values beta, v, and r are defined as follows.
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[beta, sigma, r] = ols (y, x) | Function File |
Ordinary least squares estimation for the multivariate model
y = x b + e with
mean (e) = 0 and cov (vec (e)) = kron (s, I).
where
y is a t by p matrix, x is a t by
k matrix, b is a k by p matrix, and
e is a t by p matrix.
Each row of y and x is an observation and each column a variable. The return values beta, sigma, and r are defined as follows.
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