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: 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).

Reference(s):

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

Example:

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

Result:

2-step estimates of b, s and g


Library: metrics
See also: tobit powell select sssm andrews

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: akw 06/05/96
(C) MD*TECH Method and Data Technologies, 21.9.2000