| Library: | eiv |
| See also: | eivvec1 |
| Quantlet: | eivvec2 | |
| Description: | eivvect2 calculates the maximum likelihood estimators of the parameters in the measurement error models when the entire error covariance structure is known or known up to a scalar multiple. This quantlet deals with the extension of the model considered in eivknownvaru. |
| Usage: | {hatbeta,sigmam)=eivvec2(w,y,Gamma) | |
| Input: | ||
| w | n x p matrix, the design variables | |
| y | n x 1 matrix, the response | |
| Gamma | (p+1) x (p+1) matrix | |
| Output: | ||
| hatbeta | vector, the estimate | |
| sigmam | scalar, the estimate | |
library("xplore")
library("eiv")
y=#(1.015, 1.120, 1.937, 1.743, 2.173)
w=0*matrix(5,3)
w[,1]=#(1,1,1,1)
w[,2]=#(3.442, 3.180,3.943, 3.983, 4.068)
w[,3]=#(0.477, 0.610, 0.505, 0.415, 0.620)
sig=0*matrix(4,4)
sig[,1]=#(0.5546, 0,-0.1079, -0.0691)
sig[,2]=#(0,0,0,0)
sig[,3]=#(-0.1079,0,0.2756,0.1247)
sig[,4]=#(-0.0691,0,0.1247,0.0878)
Gamma=0.01*sig
gest=eivvec2(w,y,Gamma)
gest.hatbeta
gest.sigmam
Contents of hatbeta [1,] -2.7727e+13 [2,] 0.24253 [3,] 0 Contents of sigmam [1,] 0.030575