Hi,

So firstly, I just registered right now and I need immediate help. So, I will be really grateful if someone could guide me,

Now, I'm doing a small university project and my research is basically seeing if child marriage affects higher education etc.

I was first using a LPM model with the independent variable (education) and the main explanatory variable (early marriage) as binary. Now, since it's a survey data there is a wealth index provided which is a categorical variable and a wealth score. I'm for now using the wealth score as another explanatory variable but I also want to see the effect of the wealth index as a categorical variable.

Since, LPM is very basic, I decided to do a Probit estimation as well. Now, when I run the probit regression. To get the marginal effects I know we can use the "mfx" command. But when I incorporate the wealth index which is a categorical variable, the "mfx" command no longer works. I'm just controlling for the categorical variable and my main point of interest is the early marriage binary variable. How do I estimate and interpret the coefficient of the binary variable now since the mfx command does not work?

the commands I run:

probit educ i.early_mar mar child i.windex5, robust

mfx

And I get this error: default predict() is unsuitable for marginal-effect calculation

In my model: educ =1 if a specific number of years of education have been completed or higher, early_mar=1 if married below 18 years of age, mar=1 if married, child=1 if children born, windex5 is the wealth index quintile from lowest to highest

I'll be really looking forward to a bit of help in this.

Thank you!

Regards,

Saad

So firstly, I just registered right now and I need immediate help. So, I will be really grateful if someone could guide me,

Now, I'm doing a small university project and my research is basically seeing if child marriage affects higher education etc.

I was first using a LPM model with the independent variable (education) and the main explanatory variable (early marriage) as binary. Now, since it's a survey data there is a wealth index provided which is a categorical variable and a wealth score. I'm for now using the wealth score as another explanatory variable but I also want to see the effect of the wealth index as a categorical variable.

Since, LPM is very basic, I decided to do a Probit estimation as well. Now, when I run the probit regression. To get the marginal effects I know we can use the "mfx" command. But when I incorporate the wealth index which is a categorical variable, the "mfx" command no longer works. I'm just controlling for the categorical variable and my main point of interest is the early marriage binary variable. How do I estimate and interpret the coefficient of the binary variable now since the mfx command does not work?

the commands I run:

probit educ i.early_mar mar child i.windex5, robust

mfx

And I get this error: default predict() is unsuitable for marginal-effect calculation

In my model: educ =1 if a specific number of years of education have been completed or higher, early_mar=1 if married below 18 years of age, mar=1 if married, child=1 if children born, windex5 is the wealth index quintile from lowest to highest

I'll be really looking forward to a bit of help in this.

Thank you!

Regards,

Saad

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