# 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

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### Arguments

`object` |
argument |

`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 |

`calcVCov` |
logical. If |

`returnJacobian` |
logical. If |

`...` |
currently not used. |

### 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.censReg`

has 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.

### Author(s)

Arne Henningsen

### See Also

`censReg`

, `coef.censReg`

,
and `summary.censReg`

### Examples

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