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: metrics
See also: dwade adedisdewade adeind adeslp ndw

Quantlet: adedis
Description: computes estimates of the slope coefficients in a single index model. The coefficents of the continuous variables are estimated by (an average of) dwade (density-weighted average derivative) estimates. The coefficients of the discrete explanatory variables are estimated by the method proposed in Horowitz and Haerdle, JASA 1996.

Reference(s):

Usage: {delt,alphahat,lim,hd,text}=adedis(z,x,y,h,hfac,c0,c1)
Input:
z n x d1 matrix, the observed discrete explanatory variables
x n x d2 matrix, the observed continuous explanatory variables
y n x 1 vector, the observed response variable
h scalar or d2 x 1 vector, bandwidth for dwade estimation
hfac scalar, to scale bandwidth for estimation of the link function
c0,c1 scalars, monotonicity constants
Output:
delta d2 x 1 vector, the density weighted average derivative estimates of the coefficients of the elements of x.
alphahat d1 x 1 vector, the estimates of the coefficients of the elements of z.
lim 2 x 1 vector, the limits of integration corresponding to the parameters v0 and v1 in the paper of Horowitz and Haerdle.
hd d3 x 1 vector, bandwidth for estimation of the link function for each of the d3 distinct values of the matrix z.
text string vector, output text

Example:
library("metrics")
randomize(10178)
n=250
z=(uniform(n).>0.5)~(uniform(n).<0.5)
x=normal(n)~normal(n)
ystar=1.5*z[,1]+0.25*z[,2]+1*x[,1]+2*x[,2]+normal(n)
y=(ystar>=0)
h = 0.2*(max(x)-min(x))'
hfac = 1.5
c0=0.10564
c1=0.97725
{d,a,lim,hd,text}=adedis(z,x,y,h,hfac,c0,c1)
text

Result:
Contents of text
[ 1,] "----------------------------  adedis  ----------------------"
[ 2,] "------------------------------------------------------------"
[ 3,] "                    point.est.     st.d. est.      t-stat   "
[ 4,] "------------------------------------------------------------"
[ 5,] "  alpha:  [  1]       1.6882  |     0.4655  |     0.0003"
[ 6,] "  alpha:  [  2]      -0.2122  |     0.4434  |     0.6326"
[ 7,] "                                                            "
[ 8,] "  beta :  [  1]       1.0000            -             -     "
[ 9,] "  beta :  [  2]       2.6810  |     1.0896  |     0.0146"
[10,] "------------------------------------------------------------"



Author: W.Haerdle, M.Benko, 20021022 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006