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help for margeff8                              (This version: 10 February 2006)
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Marginal effects for categorical dependent variable models

    margeff8 [compute] [if exp] [in range] [, at(atlist) count nodiscrete
            dummies(varlist_1 [\ varlist_2 ..]) eform model(stata_cmd)
            nooffset outcome(numlist) percent replace ]

    margeff8 replay

    where stata_cmd is one of

        [group 1:]   probit    logit    logistic  cloglog  heckprob  xtprobit  
>  
        [group 2:]   oprobit   ologit   gologit   mlogit   biprobit
        [group 3:]   poisson   nbreg    zip       zinb

    and atlist is
        { mean | median | zero [ varname = # [, varname = #] [...]] }
        { [mean] |[median] | [zero] varname = # [, varname = #] [...] }

    and varlist_1 [\ varlist_2 ... ] are lists of dummy variables, where all
    dummies of a list indicate different categories of the same underlying
    categorical variable.

Description

    margeff8 compute analytically estimates marginal effects, and standard
    errors for marginal effects using the delta method.  The default behavior
    of margeff8 is the following:

    1.  margeff8 calculates average marginal effects, that is, changes in the
        quantities of interest evaluated for each observations, and the
        reported marginal effects are sample averages of these changes.  Note
        that margeff8 can also compute marginal effects evaluated at sample
        means or at other values if the user specifies the at(atlist) option.
        Calculations are restricted to the estimation sample.

    2.  For continuous variables, marginal effects are partial changes in the
        quantities of interest.  For dummy variables, marginal effects are
        discrete changes in the quantities of interest as the dummy variable
        changes from 0 to 1.  Dummies are automatically detected. Before
        using margeff8, please make sure that you coded your dummy variables
        as 0/1 variables.  Otherwise your dummies will be considered as being
        continuous variables, and you run the risk of obtaining misleading
        results.  Users can change this behavior by specifying the count or
        nodiscrete options.

    3.  Quantities of interest are defined as follows:

        [group 1:] the probability of positive outcome
        [group 2:] the probabilities of all possible outcomes defined by
                   the dependent variable(s)
        [group 3:] the expected number of counts or the incidence rate

    4.  margeff8 supports only the above listed models. The model(stata_cmd)
        option, however, adds some flexibility.  This is useful if users wish
        to obtain marginal effects after the survey version of stata_cmd, or
        after their own program.

    5.  margeff8 behaves as a post-estimation command (see help postest).
        However, option replace forces margeff8 to behave as an estimation
        command (see help est). This enables the use of post-estimation
        commands like lincom or test after margeff8.

    Typed without arguments, margeff8 replays the results of the previous
    margeff8 computations, provided the last use of margeff8 was combined
    with the replace option.

    margeff8 replay replays the results of the previous margeff8 computation.

Options

    at(atlist) forces margeff8 to estimate marginal effects at points
        specified in atlist, instead of estimating average marginal effects.

        at( mean | median | zero [ varname = # [, varname = # [...]] )
        specifies that the marginal effects be evaluated at means, at medians
        of the independent variables, or at zeros.  It also allows users to
        specify particular values for one or more independent variables,
        assuming that the rest are means, medians, or zeros.

        at( [mean] |[median] | [zero] varname = # [, varname = #] [...] )
        specifies that the marginal effects be evaluated at particular values
        for one or more independent variables, assuming that the rest are
        means.

    count modifies the calculation of marginal effects for count variables,
        i.e.  variables that take more than two values and all of the values
        are integers.  By default, margeff8 treates count variables as
        continuous variables, thus marginal effects correspont to small
        changes in the independent variables.  If the count option is
        specified, the marginal effects are changes in probabilities when the
        count variables increase by unity.

    nodiscrete forces margeff8 to treat dummy variables as if they were
        continuous.  Recall that if nodiscrete is not specified, the marginal
        effect of a dummy variable is calculated as the discrete change in
        the expected value of the dependent variable as the dummy variable
        changes from 0 to 1.

    dummies(varlist_1 [\ varlist_2 ... ]) modifies the calculation of
        marginal effects for dummy variables. Let xvar be a categorical
        variables with K+1 (K>1) categories. In this case, not xvar, but K
        dummies - say, D1, ..., DK - are included in the regression model.
        The estimated marginal effects for these K dummies may be misleading
        (see the example below). The correct result is obtained if one
        specifies the dummies(D*) option.

    eform forces margeff8 to define the quantity of interest as exp(xb),
        where xb is the linear prediction.

    model(stata_cmd) forces margeff8 to estimate marginal effects as if the
        preceeding estimation command were stata_cmd.  stata_cmd must be one
        of the supported commands that are listed above.  The
        model(stata_cmd) is likely to be helpful if you wish to obtain
        marginal effects after the survey version of a supported estimation
        command, or you wish to estimate a supported model using glm.  The
        estimates, however, will be meaningful only if the estimation command
        issued by the user and stata_cmd have the same link functions.  It is
        the user's responsibility to ensure that this condition holds.

    nooffset causes margeff8 to ignore the offset variable during the
        calculations.

    outcome(numlist) causes margeff8 to display only the probability outcomes
        corresponding to the numbers specified numlist (see numlist for
        syntax guide).  This option is useful after estimation commands
        listed as [group 2] models.  The correspondence rule is the
        following:
        1. After biprobit, numbers 1 2 3 and 4 refer to outcomes p00 p01 p10
            p11.
        2. After ologit, oprobit, gologit and mlogit, number # refers to the
            #th category of the dependent variable.  Thus, number 1 always
            indicates the lowest category. And if the dependent variable has,
            say, 5 categories, then number 5 indicates the highest category.

    percent causes margeff8 to display the results in a percentage form.

    replace causes margeff8 to overwrite the estimation results left behind.
        This option is useful if
        1.  you wish to include marginal effects in publication-quality
            tables using either the official estimates table command or the
            user-written commands outreg or estout; or
        2.  your model contains a variable (such as age-squared) which is a
            mathematical transformation of another independent variable, and
            you wish to obtain the total effect of that variable (age) using
            the lincom or the nlcom command.

Remarks

    margeff8 is a work-in-progress; comments, suggestions, bug reports are
    welcome!  Please direct correspondence to the adress described at the end
    of the help file.
    To keep margeff8 up-to-date, visit the website
        http://www.uni-corvinus.hu/bartus
    or type
        net from http://www.uni-corvinus.hu/bartus/stata
        net install margeff8

 
Examples

    Illustrating the importance of the dummies( varlist_1 \ ... ) option

    Type the following commands:

    . [save mydata, replace]
    .  tabi 60 30 10 \ 20 60 20 \ 10 10 80 , replace
    .  xi: mlogit col i.row [fw=pop]
    .  margeff8

    You will see that the marginal effects do not correspond to changes in
    probabilities in the 3X3 table. Only the changes in the probabilities of
    the third outcome approximate the true changes, namely 0.1 and 0.7. The
    correct results are obtained after specifying the dummies(..) option:

    .  margeff8, dummies(_I*)

    Easy calculation of the total effect of age

    Suppose you wish to model employment status as a function of the usual
    human capital variables: gender, years of education, experience, and the
    square of experience. Assume further that the sample average of
    experience is 20. The total effect of experience on employment
    probabilities can be estimated as follows:

    . logit employed gender edu exp exp2
    .  margeff8, at(mean) replace
    .  lincom exp+40*exp2


Acknowledgements

    Some parts of the code relies on margfx (version 30 Jul 1999 for Stata 5)
    written by Jonah B. Gelbach, Dept of Economics, Univ of MD at College
    Park.

Also see

    Online:  help for est, postest; mfx


Author

    Tamas Bartus
    Department of Sociology and Social Policy,
    Corvinus University Budapest,
    Budapest, Hungary
    URL: http://www.uni-corvinus.hu/bartus
    Email: tamas.bartus@uni-corvinus.hu