QuantEcon / QuantEcon/lecture-python-intro
Add an empirical lecture on commodity supply dynamics
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- Dominant language
- Jupyter Notebook
- Stars
- 65
- Forks
- 32
- Avg merge
- 4d 14h
- Merged PRs (30d)
- 6
Description
As suggested by Serge. Use data on cotton exports from Benin?
This could be a lecture on how to estimate a Markov chain using ML, with cotton output/exports as the state. We have code for this in quantecon.py.
And/or estimate as an AR process.
Presumably there will be seasonal fluctuations, so this will have to be part of the state.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Review quantecon.py and the repository’s existing lecture notebooks first. Establish the cotton data source and the scope of the Markov-chain or AR analysis, including seasonal state variables; done means a complete empirical lecture with reproducible analysis.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- data, documentation, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100