| Library: | metrics |
| See also: | adeslp dwade trimper wtsder |
| Quantlet: | adeind | |
| Description: | indirect average derivative estimation using binning |
| 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 | |
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
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)