Library: | metrics |
See also: | tobit powell select sssm andrews |
Quantlet: | heckman | |
Description: | 2-step estimation of a regression equation in the presence of self-selection. Selection rule is of the probit type (hence, this is a Type 2 Tobit Model in chapter 10 of Amemiya's Advanced Econometrics). |
Usage: | heckit = heckman(x,y,z,q) | |
Input: | ||
x | n x d matrix , the observed explanatory variables of the regression equation | |
y | n x 1 matrix , the observed response variable of the regression equation | |
z | n x p matrix , the observed explanatory variables of the selection equation | |
q | n x 1 matrix , the observed response variable of the regression equation; | |
Output: | ||
heckit.b | d x 1 vector, contains the estimated coefficients of the components of x result of the second step | |
heckit.s | scalar, contains the estimated covariance of the error terms in the selection equation and regression equation result of the second step | |
heckit.g | p x 1 vector, contains the estimated coefficients of the components of z, result of the first step |
library("metrics") n = 500 s1 = 1 s2 = 1 s12 = 0.7 ss = #(s1,s12)~#(s12,s2) ev = eigsm(ss) va = ev.values ve = ev.vectors ll = diag(va) ll = sqrt(ll) sh = ve*ll*ve' u = normal(n,2)*sh' z = 2*normal(n,2) g = #(1,2) q = (z*g+u[,1].>=0) x = matrix(n)~aseq(1, n ,0.25) b = #(-9, 1) y = x*b+u[,2] y = y.*(q.>0) heckit = heckman(x,y,z,q) heckit.b heckit.s heckit.g
2-step estimates of b, s and g
Library: | metrics |
See also: | tobit powell select sssm andrews |