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  • Conditional marginal effect

    Dear members,

    I am trying to find marginal effects from a system of equations that I estimated using an user written command -cmp- (Roodman, 2009). I have a three stage problem: first stage is a participate/no participate decision which uses a probit model. Second stage is intensity of participation which could be 0, 1, 2 or 3; which uses an ordered probit. And the last stage is magnitude of participation ($ amount) which is a log-linear regression (this stage has four equations, one for each intensity level). After I estimate the system I want to find the marginal effect of the explanatory variables in the third stage conditional on the intensity from the second stage. Below is the code I used to estimate the system:

    Code:
    cmp ( replied =  no_target max_20 max_40 max_60 line_4 age40_65 above65 female below100000 _149999 _199999 maritalstatus data_2015 env_donor prev_mail) ///
        (multiple_add = max_40 max_60 max_100 line_4 age40_65 above65 female below100000 _149999 _199999 maritalstatus data_2015 env_donor prev_mail) ///
        ( lny3 =  no_target max_20 max_40 max_60 age40_65 above65 below100000 _149999 _199999 maritalstatus data_2015 env_donor line_4) ///
        ( lny4 =  max_20 max_40 max_60 age40_65 above65 below100000 _149999 _199999 maritalstatus data_2015 env_donor prev_mail) ///
        ( lny5 =  max_20 max_40 max_60 age40_65 above65 below100000 _149999 _199999 maritalstatus data_2015 env_donor prev_mail) ///
        ( lny6 =  max_20 max_40 max_60 age40_65 above65 below100000 _149999 _199999 maritalstatus data_2015 env_donor prev_mail), ///
        ind($cmp_probit $cmp_oprobit $cmp_cont $cmp_cont $cmp_cont $cmp_cont) nonrtolerance vce(robust) difficult
    To get the marginal effect, say for equation 3, I use the following code:
    Code:
    margins, dydx(*) predict(pr(0 .) eq(#3) cond(. _b[/cut_2_1], eq(#2)))
    To get the marginal effect for equation 4, I use the following:
    Code:
    margins, dydx(*) predict(pr(0 .) eq(#4) cond( _b[/cut_2_1] _b[/cut_2_2], eq(#2)))
    However, the codes are not running, and I am getting the following error: estimates repost: matrix has missing values. Also, I should mention here that all my explanatory variables are binary. So I think what I will get from this is average partial effects (if this code works).

    Could anyone please tell me, if this is a problem coming from the dataset, or is there something wrong with the code itself? I would really appreciate any suggestion on this.

    Thanks,
    Anwesha

  • #2
    Dear Participants,

    I am trying to make a marginal plot. My dependent variable is lnTFP (total factor productivity), independent variable is lnEU (european union agreement), RR is the regional dummies and industry2d is the industries.

    regress lnTFP lnEU RR##Industry2d
    margins Industry2d
    marginsplot, graphregion(color(white))title("Margins Plot")plotopts(connect(i))horizontal

    am I interpreting it right; the graph will show the linear prediction of the relationship between TFP and EU agreement conditioned on various regions across industries?

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