| Library: | eiv |
| See also: | eivknownatt eivknownratue |
| Macro: | eivknownvaru | |
| Description: | eivknownvaru presents the moment estimates of the parameters in the measurement error models, which has only one variable x. The variance of measurement error sigma_u is known. All of the variables obey normal distributions. See Fuller(1987), section 1.2. |
| Usage: | {mux,beta1,beta0,sigmax, sigmae} = eivknownvaru(X,Y,sigmau) | |
| Input: | ||
| X | n x 1 matrix, the design variables | |
| Y | n x 1 matrix, the response | |
| sigmau | scalar, the variance of measurement error sigma_u | |
| Output: | ||
| mux | scalar, the mean value of X | |
| beta1 | scalar, the estimate | |
| beta0 | scalar, the estimate | |
| sigmax | scalar, the estimate of the variance of X | |
| sigmae | scalar, the estimate of the variance of error e | |
library("eiv")
n = 100
randomize(n)
x= normal(n)*9
w=x+9*normal(n)
Y =0.9+0.8*x+0.01*normal(n)
sigmau=81
gest=eivknownvaru(w,Y,sigmau)
gest.mux
gest.beta1
gest.beta0
gest.sigmax
gest.sigmae
gest.mux=-0.93396; gest.beta1=0.88828 gest.beta0=0.41544 gest.sigmax=64.17; gest.sigmae=2.3624
| Library: | eiv |
| See also: | eivknownatt eivknownratue |