sampselect {endogenous}

Sample selection model (endogenous probit).

Description

James Heckman's Classical Simultaneous Equation Model (also known as the Sample Selection Model). Used to account for endogenous sample selection. Jointly models outcome model with propensity of selection, in which some of the outcomes are unobserved. Can also handle clustered data.

Usage

## S3 method for class "sampselect"
sampselect(outcome, probit, init = NULL, id = NULL, se = "R")

Arguments

outcome

an object of class "formula" with a numeric vector on the left hand side, and predictors of interest on the right hand side. Values on the left hand side that correspond to unobserved outcomes should be set to numeric values (to zero, for example, although they can be set to any numeric values).

probit

an object of class "formula" with a binary (0/1) numeric vector on the left hand side (1 indicating unobserved outcome), and predictors of selection on the right hand side (right hand side permitted to contain variables on the right hand side of the outcome equation).

init

a vector of initial values. The ordering of subparameters is: alpha (probit model parameters), beta (outcome model parameters), sigmay (outcome error standard deviation), rho (error correlation). If NULL, an initial value will be chosen through OLS linear regression and probit-link GLM without regard to endogeneity.

id

a numeric vector indicating subject IDs if data are clustered. In the absence of clustered data, this can be left blank (defaults to NULL).

se

a string, either "M" for model-based standard errors (based on inverse observed Fisher information), or "R" for robust standard errors (based on methods of Huber and White). Defaults to "R". If id is provided for clustered data, the cluster-robust variance estimator (with working independence) will be used even if the user specifies type "M".

Details

The model is evaluated with numerical minimization of the negative log-likelihood (the BFGS is used). The probit model and error correlation parameters are weakly identified and hence the error variance is set at unity. The data must be complete (no missing values) and numeric, with the exception of factors, which may be used on the right hand side of equations.

Value

sampselect prints a summary of the coefficient estimates, standard errors, Wald-based confidence intervals, and p-values for the outcome model and the selection use probit model.

alpha

estimate of the selection probit model parameters.

beta

estimate of the outcome model parameters.

sigma

estimate of the standard deviation of the outcome error.

rho

estimate of the correlation between the errors.

vcov

entire estimated variance-covariance matrix, provided if the user wishes to perform any more specific hypothesis tests.

init

initial value ultimately used, whether specified by the user or generated through the default approach.

fitted

vector of fitted outcome values.

call

the matched call.

out.form

the formula used for the outcome model.

prob.form

the formula used for the selection probit model.

sterr

the choice of the variance estimate procedure (either model-based or robust).

labels

labels for predictors to be passed into output.

Author(s)

Andrew J. Spieker, Ph.D.

References

Heckman JJ. Dummy endogenous variables in a simultaneous equation system. Econometrica 46(4), 931-959.

Maddala GS. Limited-dependent and qualitative variables in econometrics. Cambridgeshire: Cambridge University Press; 1983.

Examples

#- Generate Data -#
require(mvtnorm)
set.seed(1)
N <- 2000
X1 <- rnorm(N, 0, 1);
X2 <- rnorm(N, 0, 1);
X3 <- rnorm(N, 0, 1);
errors <- rmvnorm(N, sigma = 50*matrix(c(1, 0.5, 0.5, 1), nrow = 2))
Y <- 50 + X1 + X2 + errors[,1]
Z <- rep(0, N)
Z[(-5 + X1 + X3 + errors[,2]) > 0] <- 1
Y[Z == 1] <- 0

#- Estimate Model -#
sampselect(Y ~ X1 + X2, probit = Z ~ X1 + X3)

#- Estimate Model with Model-Based Variance -#
sampselect(Y ~ X1 + X2, probit = Z ~ X1 + X3, se = "M")