| Library: | metrics |
| See also: | adeind wtsder trimper dwade |
| Quantlet: | adeslp | |
| Description: | slope estimation of average derivatives using binning |
| Usage: | {delta,dvar} = adeslp(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} = adeslp(x,y,d,m)
delta
dvar
the slope estimator for average derivatives and its asymptotic covariance matrix as described by Stoker in Barnett, Powell, Tauchen, "Nonparametric and Semiparametric Methods in Econometrics and Statistics" (1991) and Turlach, Discussion Paper (1993)