heckit {micEcon}R Documentation

2-step Heckman (heckit) estimation

Description

heckit performs a 2-step Heckman (heckit) estimation that corrects for non-random sample selection.

Usage

   heckit( formula, probitformula, data )

Arguments

formula formula to be estimated
probitformula formula for the probit estimation (1st step)
data a data frame containing the variables in the model

Value

heckit returns an object of class 'heckit' containing following elements:

coef estimated coefficients, standard errors, t-values and p-values
vcov variance covariance matrix of the estimated coefficients
probit object of class 'glm' that contains the results of the 1st step (probit estimation).
lm object of class 'lm' that contains the results of the 2nd step (linear estimation). Note: the standard errors of this estimation are biased, because they do not account for the estimation of gamma in the 1st step estimation (the correct standard errors are returned in coef
sigma the estimated σ, the standard error of the residuals.
rho the estimated rho, see Greene (2003, p. 784).
probitLambda the λs based on the results of the 1sr step probit estimation (also known as inverse Mills ratio).
probitDelta the deltas based on the results of the 1sr step probit estimation.

Author(s)

Arne Henningsen ahenningsen@agric-econ.uni-kiel.de

References

Greene, W. H. (2003) Econometric Analysis, Fifth Edition, Prentice Hall.

Johnston, J. and J. DiNardo (1997) Econometric Methods, Fourth Edition, McGraw-Hill.

Wooldridge, J. M. (2003) Introductory Econometrics: A Modern Approach, 2e, Thomson South-Western.

See Also

lm, glm, binomial

Examples

## Greene( 2003 ): example 22.8, page 786
data( Mroz87 )
Mroz87$kids  <- ( Mroz87$kids5 + Mroz87$kids618 > 0 )
greene <- heckit( wage ~ exper + I( exper^2 ) + educ + city,
   lfp ~ age + I( age^2 ) + faminc + kids + educ, Mroz87 )
summary( greene )        # print summary
summary( greene$probit ) # summary of the 1st step probit estimation
                         # this is Example 21.4, p. 681f
greene$sigma             # estimated sigma
greene$rho               # estimated rho

## Wooldridge( 2003 ): example 17.5, page 590
data( Mroz87 )
wooldridge <- heckit( log( wage ) ~ educ + exper + I( exper^2 ),
   lfp ~ nwifeinc + educ + exper + I( exper^2 ) + age + kids5 + kids618, Mroz87 )
summary( wooldridge )        # summary of the 1st step probit estimation
                             # (Example 17.1, p. 562f) and 2nd step OLS regression
wooldridge$sigma             # estimated sigma
wooldridge$rho               # estimated rho
[Package micEcon version 0.1-3 Index]