Tutorials for Economists

One thing I have always wanted to do is write more tutorials that empirical and theoretical economists can use to inform how they do their research. @trappmartin recently reminded me how much I have wanted to do this.

This issue is meant to be a super-issue that brainstorms possible tutorials that people might like to see on how to use Turing in the social sciences (economics in particular, but I am open to other fields if someone with experience can help).

I naturally tilt towards finance and thus most of my suggestions are likely to be finance-related. I'm hoping someone with more general econ experience can jump in here. In particular, I would like to see some macroeconomics tutorials, because that has historically been where the most sophisticated applications of Bayesian tools has been.

I also want to hear from some of the QuantEcon people to see what they think. @jlperla do you know of some people who might have some good ideas for shortish tutorials they would like to see on Bayesian methods in economics? I'm not trying to get anyone to write any of these, just to solicit some ideas.

Anyways, here are some of my ideas:

1. Trend/cycle decomposition. This is a fairly simple time series analysis that let's you talk about latent variables, analytical tricks to simplify models, and ties into a moderately large literature.
2. Structural estimation of a simple macro model, like endogenous growth or something. Probably best to use a very simple model that the literature has moved on from just for computation and expository purposes.
3. An industrial organization model, also structural estimation. I don't know this literature very well but I would like to see something on market growth and consolidation.
4. A dynamic corporate finance model -- this chapter by Strebulaev and Whited is excellent and has any number of things that could be viewed from a Bayesian perspective.
5. Models that attempt to recover latent variables, perhaps like a well-structured labor econ paper that attempts to infer skill from obervables. This might also be good because a tutorial could cover DAGs and how they can be expressed in Turing.
6. A conditional beta model from finance. Lots of evidence suggests that market betas are time varying, and it would be cool to try to estimate the posteriors for conditional betas.
7. A factor model comparison paper. Barillas and Shaken (2018) do something like this where they compare a bunch of different factor models, and I think this could be done with Bayesian model combination (where expected returns are weighted by a Dirichlet distribution) to see if the results hold.
8. Structural break tests in time series (via @rlouf).
9. Conjoint MNL/mixed logit models for marketing (via this tweet).
10. Item response theory, via this tweet. There's a good Stata blog post and an ArXiV paper on this.

Any other ideas from economist types are welcome, please add them down here.

Start with the ten candidate tutorial topics and the linked papers, blog post, and examples in this issue. Consult economists and Turing users to narrow the brainstorm into a specific tutorial scope; done means an agreed, actionable tutorial proposal rather than another list of ideas.