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: adeslp dwade trimper wtsder

Macro: adeind
Description: indirect average derivative estimation using binning

Reference(s):

Usage: {delta,dvar} = adeind(x,y,d,m)
Input:
x n x p matrix , the observed explanatory variable
y n x 1 matrix , the observed response variable
d p x 1 vector or scalar , the binwidth or the grid
m p x 1 vector or scalar , the bandwidth to be used during estimation of the scores
Output:
delta p x 1 vector , the ADE estimate
dvar p x p matrix , the estimated asymptotic covariance matrix of delta

Example:
library("sim")
n   = 100
x   = normal(n,3)
z   = 0.2*x[,1] - 0.7*x[,2] + x[,3]
eps = normal(n,1) * sqrt(0.5)
y   = 2 * z^3 + eps
d   = 0.2
m   = 5
{delta,dvar} = adeind(x,y,d,m)
delta
dvar
Result:
the indirect regression estimator for average derivative
and its asymtotic covariance matrix 
as described by Haerdle and Stoker, JASA (1989)
and Turlach, Discussion Paper (1993)

Library: metrics
See also: adeslp dwade trimper wtsder

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: Turlach (in XploRe3), Sperlich & Stockmeyer 960806
(C) MD*TECH Method and Data Technologies, 28.6.1999