Some Projects that are Available for Future Work

[mmcky]

Some topics that are available to be worked on

Code Library

• Improve DiscreteDP code by porting State-Action Pair Formulation of Python code to Julia
• Improve DiscreteDP code by porting Sparse Matrix Support from Python to Julia
• CompEcon merge (see Merge in CompEcon function approximation tools #45)

Notebooks

• Fix up errors in 2 x DiscreteDP Julia notebooks
• Port python notebooks to julia equivalents in QuantEcon.notebooks() (ref Notebooks using DiscreteDP type #113)

Infrastructure

• Improve testing infrastructure by running Julia notebooks and checking for any runtime errors. QuantEcon.applications contains some starting code which is used to run python notebooks using the runipy project

[albep]

I should've fixed the issue in ddp_theory_jl.ipynb. The only other Julia notebook is ddp_ex_job_search_jl.ipynb but I don't see errors there. Are there other notebooks with errors?

[mmcky]

thanks @albep for the update. I will review and merge your PR today. There are some python notebooks that also need to be ported to Julia. For example:

https://github.com/QuantEcon/QuantEcon.applications/blob/master/discrete_dp/discrete_dp_solutions_py.ipynb

needs to be ported to julia so that we can post a DiscreteDP lecture for the Julia side of the website.

[albep]

@mmcky That sounds good, I'll start working on porting the notebooks

[jstac]

Thanks @albep . If you step through the Julia lectures you might find others that are missing solution notebooks.

@mmcky Thanks for organizing.

[albep]

I've implemented the State-Action Pair Formulation in ddp.jl. I've also tested it a bit and, for instance, it matches the results in this notebook: https://github.com/QuantEcon/QuantEcon.applications/blob/master/discrete_dp/discrete_dp_solutions_py.ipynb

I'm sure someone more proficient than me with Julia can polish it, but it could be a starting point. Before I start implementing Sparse Matrix Support, would anyone like to take a look at it? Shall I proceed with a PR?

[sglyon]

Good work!

Let's open up a pull request and I'll look at it. Thanks

// Spencer
On Jan 23, 2016 5:31 PM, "albep" notifications@github.com wrote:

I've implemented the State-Action Pair Formulation in ddp.jl. I've also
tested it a bit and, for instance, it matches the results in this notebook:
https://github.com/QuantEcon/QuantEcon.applications/blob/master/discrete_dp/discrete_dp_solutions_py.ipynb

I'm sure someone more proficient than me with Julia can polish it, but it
could be a starting point. Before I start implementing Sparse Matrix
Support, would anyone like to take a look at it? Shall I proceed with a PR?

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Reply to this email directly or view it on GitHub
#83 (comment)
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[Dawny33]

I would like to help with code porting from Python to Julia.

So, if they aren't already being worked upon, I'd be up for it!

[sglyon]

Glad you want to help out! right now I don't think there are any open issues for things that need to be ported from Python to Julia.

@QuantEcon/lead-developers can anyone else think of one that I'm missing?

That being said there's plenty of work to be done if you are interested in helping out.

[oyamad]

cyclic_classes is missing for MarkovChain. The task will be to port the cyclic_classes method of DiGraph to Julia (and perhaps include it in LightGraphs.jl) and call it from mc_tools.jl. See also #32 and #56.

[thomassargent30]

Dear Spencer, what about the lectures on optimal taxes with incomplete
markets? Tom
On Mar 8, 2016 23:06, "Spencer Lyon" notifications@github.com wrote:

Glad you want to help out! right now I don't think there are any open
issues for things that need to be ported from Python to Julia.

@QuantEcon/lead-developers
https://github.com/orgs/QuantEcon/teams/lead-developers can anyone else
think of one that I'm missing?

That being said there's plenty of work to be done if you are interested in
helping out.

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[sglyon]

Dear Tom,

Yes this is a great idea! I had forgotten about the lecture material.

I quickly compared the table of contents on the Python side to the Julia side and saw that we are missing the following lectures on the Julia side:

• The lake model (python code here)
• "Adding bling" to the growth model (python code here)
• Markov perfect equilibria: I believe we have the julia core algorithm code for this (nnash function in QuantEcon.jl), but maybe not for all the examples. Python code here
• The Aiyagari Model. This relies on the DDP code, which we are almost finished with. If you are interested in working on this please let me know as that will provide motivation to finish the ddp codes sooner. Python code here
• Dynamic Stackelberg Problems. Python code here
• Optimal Taxation with State-Contingent Debt. Python code here
• Optimal Taxation without State-Contingent Debt. Python code here

Feel free to tackle any of these!

[jstac]

@spencerlyon2 Thanks, this is a helpful list.

@Dawny33 Pease do feel free to tackle any of these. Note that the relevant repo for development and PRs is for the most part QuantEcon.applications rather than this one.

[Dawny33]

@spencerlyon2 Wow thank you, that's a long list! Would love to work on some of them!

@jstac Noted. Thanks for the heads-up!

[arnavs]

@jstac @mmcky I'd be happy to bite off whatever I can chew. You guys have some familiarity with what I can do, so let me know where (if anywhere) I might be helpful.

[jstac]

Help is definitely appreciated.

I suggest that you take more time to read through the library first, trying different parts, experimenting, looking at everyone's code. Try to pick up their style of coding. Maybe have a look at the tests and see if you can improve them. Improving tests and documentation is a good place to start.

[arnavs]

@jstac Sounds good. I'll start getting myself acquainted with the code.