Structural estimation in Stata
This goes to the ones who already know what they want to do, and it has to do with structural modeling. It’s about how to do this in Stata (of all places).
There are many reasons why you may want to use Stata for your empirical analysis, from beginning to end. Usually, you will use Stata anyways to put together your data set and also to do your descriptive analysis–it’s just so much easier than many other packages because many useful tools come with it. Plus, it’s a quasi industry standard among economists, so using it and providing code will be most effective.
So, if your structural model is not all that complicated, you can just as well estimate it in Stata.
Today, I want to point you to two useful guides for that. The first one is the guide by Glenn Harrison. This is actually how I first learned to program up a simulated maximum likelihood estimator. It’s focused around experiments and the situation you usually have there, namely choices between two alternatives. It’s a structural estimation problem because each alternative will generate utility, and the utility function depends on parameters that we seek to estimate.
Then, today I bumped into the lecture notes by Simon Quinn, which I found particularly insightful and useful if what you’re doing has components of a life cycle model. What I like particularly about his guide is that it explains how you would make some choices related to the specification of your model and functional forms.
Of course, there are also many reasons why you may not want to use Stata for your analysis. But in any case, it may not hurt to give it a thought.