Open DawievLill opened 2 years ago
@DawievLill Wow, that is a lot.
There are a lot of directions to go, and plenty of possible reorganization, but generally the best approach might be to implement stuff as you need them for teaching. For example, the introduction to dynamics might be a good one if you were thinking of teaching that to advanced undergrads/etc.
@jlperla Thanks for the advice.
I am teaching a 3rd year macroeconomics course next year, so that seems like a good opportunity to better integrate the lectures into my teaching.
The idea with the list was primarily to give some notion of how many lectures can still be ported. I only realised after putting it together how many there were!
As an aside, if I am working on a lecture should I create a new branch and then merge it once I do the pull request?
Thanks @DawievLill
Yeah, generally the way to do it is to create a new branch, get things working the way you like them, and make a pull request (and maybe say WIP
in it until you are done).
For the lecture organization, I think that for now you could just put them in whatever directory makes the most sense for now, add in a reference within https://github.com/QuantEcon/lecture-julia.myst/blob/main/lectures/_toc.yml and then we can consier reorganizing later.
The key thing, though, would be to have one-branch per lecture. pull requests shouldn't get too big.
Thanks for the list @DawievLill , it's really useful. I didn't realize there were so many to be ported.
As @jlperla said, fitting with your teaching is a good approach. Given that you are going to be teaching 3rd year macro, the "Introduction to Dynamics" section might be good. I've taught that to advanced undergraduates and they found it quite hard, since it's quite conceptual. But it's certainly within reach if you teach it slowly and give students time to experiment.
Hi,
I mentioned on the QE discourse that I was looking at porting some of the Python code to Julia and was told to open an issue.
Listed below are lectures that could potentially be ported over to Julia.
It is possible that I have missed some lectures and that some of the content of these lectures have been incorporated into existing Julia lectures.
If there is a specific request for a lecture to be ported I would be more than happy to do it. My general plan is to start with the lectures in sequence as they are presented here, but I am open to suggestions on a more systematic approach to the process.
Tools and techniques
Linear Programming
Introduction to Dynamics
Should the dynamic programming section be split up in a similar way to the Python section? I have kept it intact for now, but this could be revised.
Dynamic Programming
Information
Multiple Agent Models
Asset pricing and Finance
Data and Empirics
Dynamic Linear Economies
Classic Linear Models
Time Series Models
Dynamic Programming Squared