Marginal effect of interaction variable in probit regression using Stata -


i running probit regression interaction between 1 continuous , 1 dummy variable. coefficient displayed in regression output when @ marginal effects interaction missing.

how can marginal effect of interaction variable?

probit move_right c.real_income_change_percent##i.gender  iteration 0:   log likelihood = -345.57292   iteration 1:   log likelihood = -339.10962   iteration 2:   log likelihood = -339.10565   iteration 3:   log likelihood = -339.10565    probit regression                                 number of obs   =        958                                                   lr chi2(3)      =      12.93                                                   prob > chi2     =     0.0048 log likelihood = -339.10565                       pseudo r2       =     0.0187  -----------------------------------------------------------------------------------------------------                          move_right |      coef.   std. err.      z    p>|z|     [95% conf. interval] ------------------------------------+----------------------------------------------------------------          real_income_change_percent |   .0034604   .0010125     3.42   0.001      .001476    .0054448                                     |                              gender |                             female  |   .0695646   .1139538     0.61   0.542    -.1537807    .2929099                                     | gender#c.real_income_change_percent |                             female  |  -.0039908   .0015254    -2.62   0.009    -.0069805   -.0010011                                     |                               _cons |  -1.263463   .0798439   -15.82   0.000    -1.419954   -1.106972 -----------------------------------------------------------------------------------------------------   margins, dydx(*) post  average marginal effects                          number of obs   =        958 model vce    : oim  expression   : pr(move_right), predict() dy/dx w.r.t. : real_income_change_percent 1.gender  --------------------------------------------------------------------------------------------                            |            delta-method                            |      dy/dx   std. err.      z    p>|z|     [95% conf. interval] ---------------------------+---------------------------------------------------------------- real_income_change_percent |   .0002846   .0001454     1.96   0.050    -4.15e-07    .0005697                            |                     gender |                    female  |  -.0102626   .0207666    -0.49   0.621    -.0509643    .0304392 -------------------------------------------------------------------------------------------- note: dy/dx factor levels discrete change base level.

your question seems strange me. asked dummy-dummy interaction, example involves continuous-dummy interaction.

here's how either one:

webuse union, clear  /* dummy-dummy iteraction */ probit union i.south##i.black grade, nolog margins r.south#r.black  /* continuous-dummy iteraction */ probit union i.south##c.grade margins r.south, dydx(grade)

you should try reproduce these "hand" (using differences of predicts) understand margins command doing behind scenes.


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