Recursive system, OLS and Stata estimation

[Arjen Steiner]

Hello everyone, I replicate a study on migration and I would like to know how to correctly estimate the paper's equation system with Stata.

In fact, the insufficient information they give does not allow us to know which command we should use in order to perform the following operations correctly :


The system :

(1) M_i= β₀ + β₁D_i + β₂P_i + β₃E +εi

(2) H_i= β₀ + β₁M_i + β₂M_i *G_i + β₃E +εi

_{(3) GDPi}= β₀ + β₁M_i + β₂H_i + β₃R +ε_i

The additional informations are the following:

• "The three equation system is a recursive one, so there is no simultaneity bias."
• "Recursive one so there is no simultaneity bias"
• "we use OLS"

My question is: should I use sem or gsem command to estimate it, as it is a recursive system? (note that there is an interaction term in (2)) or just a 2sls?

They explicitly said that they use OLS:

- Shoud I use a simple OLS and put the predicted values of Mi & Hi in the next equations (2) & (3) ?
If so, is there a way to correct for the variance of the generated regressors?

I am confused regarding the right command to use, I will appreciate your precious help !

[Eric Makela]

Hello Arjen, it seems to me you could use either the 'sem' or 'reg' commands, combined with 'predict' to solve this issue. If you're seeking simplicity, you can create the interaction variables yourself or use Stata's '#' syntax in the regression command. I'd suggest you viewed Stata's official documentation for those commands, and see what most closely matches your chosen academic paper.

Are you familiar with how to read an academic study, making sure you can use Stata's data management tools to re-create the sample?