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 |
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")
The parameter estimates, see Jiti Gao, Shengyan Hong and Hua Liang" Convergence rate in partly linear models", Acta Mathematical Sinica (1995) 17, 170-180.