margEff.censReg: Marginal Effects in Censored Regression Models

Description

The margEff method computes the marginal effects of the explanatory variables on the expected value of the dependent variable evaluated. Please note that this functionality is currently not available for panel data models.

Usage

# S3 method for censReg
margEff( object, xValues = NULL, vcov = NULL,
   calcVCov = TRUE, returnJacobian = FALSE, vcovLogSigma = TRUE, ... )
# S3 method for margEff.censReg
summary( object, ... )

Value

margEff.censReg returns an object of class "margEff.censReg", which is a vector of the marginal effects of the explanatory variables on the expected value of the dependent variable evaluated at the mean values of the explanatory variables. The returned object has an attribute df.residual, which is equal to the degrees of freedom of the residuals.

If argument calcVCov is TRUE, the object returned by margEff.censReg has an attribute vcov, which is a the approximate variance covariance matrices of the marginal effects calculated with the Delta method.

If argument returnJacobian is TRUE, the object returned by margEff.censReghas an attribute jacobian, which is the Jacobian of the marginal effects with respect to the coefficients.

summary.margEff.censReg returns an object of class "summary.margEff.censReg", which is a matrix with four columns, where the first column contains the marginal effects, the second column contains the standard errors of the marginal effects, the third column contains the corresponding t-values, and the fourth columns contains the corresponding P values.

Arguments

object: argument object of the margEff method must be an object of class "censReg" (returned by censReg); argument object of the summary method must be an object of class "margEff.censReg" (returned by margEff.censReg).
xValues: vector that specifies the values of the explanatory variables (including the intercept if it is included in the model), at which the marginal effects should be calculated. The number and order of the elements of this vector must correspond to the number and order of the estimated coefficients (without sigma). If this argument is NULL (or not specified), argument xValues is set to the mean values of the explanatory variables.
vcov: a symmetric matrix that specifies the variance covariance matrix of the estimated coefficients that should be used to calculate the variance covariance matrix and the standard errors of the marginal effects. If this argument is NULL (the default), the variance covariance matix is obtained by vcov( object ).
calcVCov: logical. If TRUE, the approximate variance covariance matrices of the marginal effects is calculated and returned as an attribute (see below).
returnJacobian: logical. If TRUE, the Jacobian of the marginal effects with respect to the coefficients is returned as an attribute (see below).
vcovLogSigma: logical. TRUE (the default) indicates that the last row and last column of the variance covariance matrix provided by argument vcov indicate the (co)variances of the logarithm of the sigma coefficient, while FALSE indicates that this row and this column indicate the (co)variances of the (non-logarithic) sigma coefficient. If argument vcov is NULL, argument vcovLogSigma is ignored.
...: currently not used.

Author

Arne Henningsen

Examples

Run this code

## Kleiber & Zeileis ( 2008 ), page 142
data( "Affairs", package = "AER" )
estResult <- censReg( affairs ~ age + yearsmarried + religiousness +
   occupation + rating, data = Affairs )
margEff( estResult )
summary( margEff( estResult ) )
margEff( estResult, xValues = c( 1, 40, 4, 2, 4, 4 ) )