__Loadable Function:__[`x`,`obj`,`info`,`lambda`] =**qpsol***(*`x`,`H`,`c`,`lb`,`ub`,`lb`,`A`,`ub`)- Solve quadratic programs using Gill and Murray's QPSOL. Because QPSOL is not freely redistributable, this function is only available if you have obtained your own copy of QPSOL. See section Installing Octave.

__Loadable Function:__**qpsol_options***(*`opt`,`val`)-
When called with two arguments, this function allows you set options
parameters for the function
`qpsol`

. Given one argument,`qpsol_options`

returns the value of the corresponding option. If no arguments are supplied, the names of all the available options and their current values are displayed.

__Loadable Function:__[`x`,`obj`,`info`,`lambda`] =**npsol***(*`x`,`phi`,`lb`,`ub`,`lb`,`A`,`ub`,`lb`,`g`,`ub`)-
Solve nonlinear programs using Gill and Murray's NPSOL. Because NPSOL
is not freely redistributable, this function is only available if you
have obtained your own copy of NPSOL. See section Installing Octave.
The second argument is a string containing the name of the objective function to call. The objective function must be of the form

y = phi (x)

where x is a vector and y is a scalar.

__Loadable Function:__**npsol_options***(*`opt`,`val`)-
When called with two arguments, this function allows you set options
parameters for the function
`npsol`

. Given one argument,`npsol_options`

returns the value of the corresponding option. If no arguments are supplied, the names of all the available options and their current values are displayed.

__Function File:__**gls***(*`Y`,`X`,`O`)-
Generalized least squares (GLS) estimation for the multivariate model
Y = X * B + E, mean(E) = 0, cov(vec(E)) = (s^2)*O

with

Y an T x p matrix X an T x k matrix B an k x p matrix E an T x p matrix O an Tp x Tp matrix

Each row of Y and X is an observation and each column a variable.

Returns BETA, v, and, R, where BETA is the GLS estimator for B, v is the GLS estimator for s^2, and R = Y - X*BETA is the matrix of GLS residuals.

__Function File:__**ols***(*`Y`,`X`)-
Ordinary Least Squares (OLS) estimation for the multivariate model
Y = X*B + E, mean (E) = 0, cov (vec (E)) = kron (S, I)

with

Y an T x p matrix X an T x k matrix B an k x p matrix E an T x p matrix

Each row of Y and X is an observation and each column a variable.

Returns BETA, SIGMA, and R, where BETA is the OLS estimator for B, i.e.

BETA = pinv(X)*Y,

where pinv(X) denotes the pseudoinverse of X, SIGMA is the OLS estimator for the matrix S, i.e.

SIGMA = (Y - X*BETA)'*(Y - X*BETA) / (T - rank(X))

and R = Y - X*BETA is the matrix of OLS residuals.

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