Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: plm
See also: plmhett plmhetexog

Macro: plmhetmean
Description: plmhetmean estimates the parameter part in partially linear heteroscedastic models, in which the variance is an unknown function of the mean. We use the replication technique to estimate the variance functions.

Usage: res = plmhetmean(mn,x,t,y,h)
Input:
mn scalar, replicate
x n x p matrix, the design
t n x 1 matrix, the design in [0, 1]
y n x mn matrix, the response
h p x 1 matrix or scalar, chosen bandwidth
Output:
res.hbetals p x 1 matrix, LS estimate of parameter
res.hbeta p x 1 matrix, the estimate based on our method
res.hg0 n x 1 matrix, estimate of nonparameter function based on res.hbetals
res.hg n x 1 matrix, estimate of nonparameter function based on res.hbeta

Example:
library("plm")
randomize(100)
n = 100
mn = sqrt(n)
sig=0*matrix(3,3)
sig[,1]=#(0.81,0.1,0.2)
sig[,2]=#(0.1,2.25,0.1)
sig[,3]=#(0.2,0.1,1)
x =normal(n,3)*sig
t =sort(uniform(n))
beta0=#(1.2, 1.3, 1.4)  ; the true value
ma = x*beta0+t^3
y =ma+0.01*(ma+1/(1+ma)).*normal(n,mn)
h =0.25
res=plmhetmean(mn,x,t,y,h)
res.hbetals
res.hbeta
ddpt=createdisplay(1,1)
datah1=t~t^3
datah2=t~res.hg0
datah3=t~res.hg
part=grid(1,1,rows(t))'
setmaskp(datah1,1,0,1)
setmaskp(datah2,4,0,3)
setmaskp(datah3,7,0,5)
setmaskl(datah1,part,1,1,1)
setmaskl(datah2,part,4,1,3)
setmaskl(datah3,part,2,1,1)
show(ddpt,1,1,datah1,datah2,datah3)
setgopt(ddpt,1,1,"xlabel","T","title","Simulation comparison","ylabel","g(T) and its estimate values")
Result:
The parameter estimates, see Hua Liang and Wolfgang
Haerdle" Asymptotic normality of parametric regression part in
partial linear heteroscedastic regression models",
DP 970033 of SFB 373.

Library: plm
See also: plmhett plmhetexog

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Hua Liang, 12.05.1998
(C) MD*TECH Method and Data Technologies, 17.8.2000