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: plmhetexog plmhetmean

Macro: plmhett
Description: plmhett estimates the parameter part in partially linear heteroscedastic models, in which the variance is an unknown function of nonparametric variables

Usage: res = plmhett(x,t,y,h,{h1})
Input:
x n x p matrix, the design
t n x 1 matrix, the design in [0, 1]
y n x 1 matrix, the response
h p x 1 matrix or scalar, chosen bandwidth
h1 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

Example:
library("plm")
randomize(100)
n = 100
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
y =x*beta0+t^3+0.1*(t+5/(1+t)).*normal(n)
h =0.15
res=plmhett(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: plmhetexog plmhetmean

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, 28.6.1999