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. |
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 |
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
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,] "------------------------------------------------------------"