Add python/julia codes for solving NK Model in CLMM #53
Conversation
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This would be a very good addition
…On Jun 3, 2017 12:03, "Spencer Lyon" ***@***.***> wrote:
In this PR I included two notebooks that serve as part of the appendix for
a working paper I'm working on with @cc7768 <https://github.com/cc7768>
and @maliars <https://github.com/maliars>.
The notebooks provide code for solving a New Keynesian model with an
option to impose or not impose a zero lower bound on interest rates.
Once the paper has a home somewhere online I will update these notebooks
to include a link to the paper.
@cc7768 <https://github.com/cc7768> might have a couple more notebooks
coming that are the other part of our Python/Julia appendix demonstrating 7
different algorithms for solving a standard growth model.
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You can view, comment on, or merge this pull request online at:
#53
Commit Summary
- Add python/julia codes for solving NK Model in CLMM
File Changes
- *A* NKModel_CLMM_julia.ipynb
<https://github.com/QuantEcon/QuantEcon.notebooks/pull/53/files#diff-0>
(968)
- *A* NKModel_CLMM_python.ipynb
<https://github.com/QuantEcon/QuantEcon.notebooks/pull/53/files#diff-1>
(828)
Patch Links:
- https://github.com/QuantEcon/QuantEcon.notebooks/pull/53.patch
- https://github.com/QuantEcon/QuantEcon.notebooks/pull/53.diff
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These notebooks are excellent. There is lots of useful code, but seem a little sparse in terms of words. If these will be "stand alone" notebooks on the QE notebooks site, it might be helpful to provide a little more description of the model and solution. (I guess you could counter this suggestion by saying that the details are in the paper and can be found there). |
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I was also a little torn about having np description of the model, but after having thought about it I see it as more of an advantage. This is an online appendix and the paper contains the model. If you think it would be better I would be happy to include the list of equilibrium conditions and variables (similar to what we do in the main body of the paper right now). Thoughts? |
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@sglyon Many thanks. Very interesting. In my view we could do without all the equations as long as we have a link to the paper embedded in the notebook. Do you have a permanent URL? Also, at the end of the computations I was looking forward to seeing interpretation. I suppose that's in the paper and if so that's fine --- there's no need to repeat. But perhaps at the end of each notebook a line that says interpretation can be found in the paper would be good. |
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Hey @jstac that's a good idea. I'll at least add a line directing people to the paper if they want to learn more and see some interpretation. Once we hear @cc7768's vote about the equations, we can make final decision about that. We don't yet have a permanent URL, but when we do I'll revise these notebooks and put it in. |
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I could live without the equations if we eventually can point to a place where people could investigate if they were interested in them. I guess my opinion that it is sparse on words depends on whether this is meant solely as a computational appendix (i.e. a place for people to read and explore the code after having viewed the paper) or whether this is meant to be a standalone notebook people may stumble across on QE. My interpretation was that we would hope for interest generated in both directions, if that is true, I think the notebook could use a few more words. For example, in the "Solution Routine" section there are no words that describe what is happening. A couple words in the solution routine section and a "conclusion" paragraph would add some value (even though a lot of it would already be said in the paper). |
In this PR I included two notebooks that serve as part of the appendix for a working paper I'm working on with @cc7768 and @maliars.
The notebooks provide code for solving a New Keynesian model with an option to impose or not impose a zero lower bound on interest rates.
Once the paper has a home somewhere online I will update these notebooks to include a link to the paper.
@cc7768 might have a couple more notebooks coming that are the other part of our Python/Julia appendix demonstrating 7 different algorithms for solving a standard growth model.