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help for _grmargb                                                     08AUG2009
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Marginal effects and their differences from binary regressions using bootstrap 
> method for inference

        _grmargb [if] [in] [, x(variables_and_values) rest(stat) level(#)
               reps(#) size(#) save diff nobase all match dots]

    where variables_and_values is an alternating list of variables and either
    numeric values or mean, median, min, max, upper, lower, previous and stat
    is either mean, median, min, max, upper, lower, previous, grmean(group
    mean), mrmedian, grmin, grmax.

 Description

    _grmargb is a command that returns marginal effects, differences in
    marginal effects and their confidence intervals using bootstrap method
    with resampling technique. It can calculate boostrapped confidence
    intervals using the normal approximation, percentile, and bias-corrected
    methods.

 Options

    x(variables_and_values) sets the values of independent variables for
        calculating predicted values (marginal effects). The list must
        alternate variable names and either numeric values or types of stat.

    rest(stat) sets the independent variables not specified in
        x(variables_and_values) to one of the types of stat.

    level() sets the level of the confidence interval for differences in
        group average marginal effects. The default is 95.

    reps(#) specifies the number of bootstrap replications to be performed.
        The default is 1000.

    size(#) specifies the size of the samples to be drawn.  The default is
        e(N), the same size as the estimation sample.

    save saves current values of independent variables and predictions for
        computing changes using the diff option.

    diff computes difference between current predictions and those that were
        saved.

    nobase suppresses inclusion of the base values of x in the output.

    all specifies that any calculation of means, medians, etc., should use
        the entire sample instead of the sample used to estimate the model.

    match requests grmargb to resample from each category group of the
        dependent variable in proportion of the resample size to the original
        sample size.

    dots requests a dot be placed on the screen at the beginning of each
        replication, thus providing entertainment when a large number of
        reps() are requested. It also prints out the percent replications
        finished.

 Returned Matrices

    r(marg):  saves marginal effects.

    r(margci):  saves confidence intervals for marginal effects. Column 1 - 6
        correspond to lower bounds, upper bounds for percentile normal
        approximation, and bias-corrected methods.

    r(margvar): saves variance-covariance matrix for marginal effects

    r(margse):  saves standard errors for marginal effects

    r(dmarg):  saves differences in marginal effects when diff option is used

    r(dmargci): saves confidence intervals for differences in marginal
        effects when diff option is used. Column 1 - 6 correspond to lower
        bounds, upper bounds for percentile normal approximation, and
        bias-corrected methods.



Examples

    To compute the predicted marginal effects, differences in marginal
    effects, and their confidence intervals using bootstrap method for a
    logit model. All independent variables are held at their means except for
    black and education specifed in x().

    .logit vote black educ income

    .grmargb, x(black=1 educ=16) save

    .grmargb, x(black=0 educ=16) diff

    :::

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  Authors: Jun Xu