Node:Linear Least Squares, Previous:Nonlinear Programming, Up:Optimization



Linear Least Squares

[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.

beta
The GLS estimator for b.
v
The GLS estimator for s^2.
r
The matrix of GLS residuals, r = y - x beta.

[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.

beta
The OLS estimator for b, beta = pinv (x) * y, where pinv (x) denotes the pseudoinverse of x.
sigma
The OLS estimator for the matrix s,
sigma = (y-x*beta)'
  * (y-x*beta)
  / (t-rank(x))

r
The matrix of OLS residuals, r = y - x * beta.