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ENH: Fix for bringing code in from a file (#16)
* use ifp as a test case' * update lecture files to new syntax for executable code from file * install myst_nb from the branch * remove debug cell at the top of lecture * install mysb_nb from branch for testing * add mathjax cdn address * update sources lecture-python (9ae923d) using tomyst (b06cacb) * fix for newline bug in wald_friedman * update config for latest sphinxcontrib-bibtex (#84) * update environment for testing from branch mystnb:code-from-file * upload build folder as an artifact * fix path * update timeout for exection to 10min * migrate from :file: to :load: (myst-nb) * remove install of myst_nb from branch and include latest jupyter-book master install
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‎.github/workflows/ci.yml‎

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@@ -32,6 +32,11 @@ jobs:
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shell: bash -l {0}
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run: |
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jb build lectures --path-output ./
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- name: Save Build as Artifact
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uses: actions/upload-artifact@v1
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with:
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name: _build
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path: _build
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- name: Preview Deploy to Netlify
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uses: nwtgck/actions-netlify@v1.1
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with:

‎.gitignore‎

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lectures/_build
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_build/
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lectures/_build/

‎lectures/_config.yml‎

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@@ -5,7 +5,10 @@ description: This website presents a set of lectures on quantitative economic mo
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execute:
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execute_notebooks: "cache"
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timeout: 60
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timeout: 600
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bibtex_bibfiles:
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- _static/quant-econ.bib
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bibtex_bibfiles:
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- _static/quant-econ.bib
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og_logo_url: https://assets.quantecon.org/img/qe-og-logo.png
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description: This website presents a set of lectures on quantitative economic modeling, designed and written by Thomas J. Sargent and John Stachurski.
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keywords: Python, QuantEcon, Quantitative Economics, Economics, Sloan, Alfred P. Sloan Foundation, Tom J. Sargent, John Stachurski
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mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
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rediraffe_redirects:
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index_toc.md: intro.md
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tojupyter_static_file_path: ["source/_static", "_static"]

‎lectures/cake_eating_numerical.md‎

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The analytical solutions for the value function and optimal policy were found
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to be as follows.
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```{literalinclude} _static/lecture_specific/cake_eating_numerical/analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/cake_eating_numerical/analytical.py
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```
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Our first aim is to obtain these analytical solutions numerically.

‎lectures/coleman_policy_iter.md‎

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This will allow us to compare our results to the analytical solutions
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```{literalinclude} _static/lecture_specific/optgrowth/cd_analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/cd_analytical.py
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```
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As discussed above, our plan is to solve the model using time iteration, which
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These are available in a class called `OptimalGrowthModel` that we
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constructed in an {doc}`earlier lecture <optgrowth_fast>`.
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```{literalinclude} _static/lecture_specific/optgrowth_fast/ogm.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth_fast/ogm.py
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```
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Now we implement a method called `euler_diff`, which returns
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`OptimalGrowthModel` and returns an approximation to the optimal policy,
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using time iteration.
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```{literalinclude} _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```{code-cell} python3
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:load: _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```
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Let's call it:
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We use the class `OptimalGrowthModel_CRRA` from our {doc}`VFI lecture <optgrowth_fast>`.
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```{literalinclude} _static/lecture_specific/optgrowth_fast/ogm_crra.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth_fast/ogm_crra.py
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```
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Let's create an instance:

‎lectures/egm_policy_iter.md‎

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This will allow us to make comparisons with the analytical solutions
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```{literalinclude} _static/lecture_specific/optgrowth/cd_analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/cd_analytical.py
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```
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We reuse the `OptimalGrowthModel` class
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```{literalinclude} _static/lecture_specific/optgrowth_fast/ogm.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth_fast/ogm.py
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```
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### The Operator
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Here's our solver routine:
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```{literalinclude} _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```{code-cell} python3
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:load: _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```
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Let's call it:

‎lectures/ifp.md‎

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The following function iterates to convergence and returns the approximate
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optimal policy.
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```{literalinclude} _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```{code-cell} python3
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:load: _static/lecture_specific/coleman_policy_iter/solve_time_iter.py
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```
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Let's carry this out using the default parameters of the `IFP` class:
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We know that, in this case, the value function and optimal consumption policy
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are given by
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```{literalinclude} _static/lecture_specific/cake_eating_numerical/analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/cake_eating_numerical/analytical.py
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```
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Let's see if we match up:

‎lectures/markov_perf.md‎

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From these, we compute the infinite horizon MPE using the preceding code
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```{literalinclude} _static/lecture_specific/markov_perf/duopoly_mpe.py
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```{code-cell} python3
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:load: _static/lecture_specific/markov_perf/duopoly_mpe.py
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```
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Running the code produces the following output.

‎lectures/optgrowth.md‎

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In Python, the functions above can be expressed as:
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```{literalinclude} _static/lecture_specific/optgrowth/cd_analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/cd_analytical.py
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Next let's create an instance of the model with the above primitives and assign it to the variable `og`.
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We can write a function that iterates until the difference is below a particular
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```{literalinclude} _static/lecture_specific/optgrowth/solve_model.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/solve_model.py
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```
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Let's use this function to compute an approximate solution at the defaults.

‎lectures/optgrowth_fast.md‎

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As before, we will be able to compare with the true solutions
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```{literalinclude} _static/lecture_specific/optgrowth/cd_analytical.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/cd_analytical.py
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## Computation
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This is where we sacrifice flexibility in order to gain more speed.
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```{literalinclude} _static/lecture_specific/optgrowth_fast/ogm.py
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:load: _static/lecture_specific/optgrowth_fast/ogm.py
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The class includes some methods such as `u_prime` that we do not need now
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```{literalinclude} _static/lecture_specific/optgrowth/solve_model.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth/solve_model.py
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Let's compute the approximate solution at the default parameters.
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Here's our CRRA version of `OptimalGrowthModel`:
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```{literalinclude} _static/lecture_specific/optgrowth_fast/ogm_crra.py
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```{code-cell} python3
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:load: _static/lecture_specific/optgrowth_fast/ogm_crra.py
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Let's create an instance:

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