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

Quantlet: 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("metrics")

randomize(0)

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, 21.9.2000