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: plmlorg plmp

Quantlet: plmk
Description: plmk estimates the parameter part in partially linear models by using kernel to approximate the nonparametric part

Usage: res = plmk(x,t,y,h)
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
Output:
res.hbeta p x 1 matrix, estimate of parameter
res.hsigma scalar, estimate of variance
res.hg n x 1 matrix, estimate of nonparameter function

Example:

library("plm")

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+0.01*normal(n)

h =0.5

res=plmk(x,t,y,h)

res.hbeta               ; the estimate of beta

res.hsigma              ; the estimate of the variance when error is homoscedastic

ddp=createdisplay(1,1)

datah1=t~t^3

datah2=t~res.hg

part=grid(1,1,rows(t))'

setmaskp(datah1,1,0,1)

setmaskp(datah2,4,0,3)

setmaskl(datah1,part,1,1,1)

setmaskl(datah2,part,4,1,3)

show(ddp,1,1,datah1,datah2)

setgopt(ddp,1,1,"xlabel","T","title","Simulation comparison","ylabel","g(T) and its estimate values")

Result:

The parameter estimates, see Jiti Gao, Shengyan Hong and

Hua Liang" Convergence rate in partly linear models",

Acta Mathematical Sinica (1995) 17, 170-180.


Library: plm
See also: plmlorg plmp

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, 98
(C) MD*TECH Method and Data Technologies, 21.9.2000