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Commit 1908695

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mmckymmcky
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MAINT: upgrade anaconda=2025.06 and python=3.13 (#503)
* MAINT: upgrade anaconda=2025.06 and python=3.13 * FIX: figure -> image directive in excercises * tmp: disable build cache full execution run * upgrade quantecon==0.10.0 * upgrade quantecon-book-theme=0.9.0 x quantecon==0.10.0 (lecture install) * Revert "tmp: disable build cache full execution run" This reverts commit 9f1adc2.
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‎.github/workflows/cache.yml‎

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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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python-version: "3.13"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro, PyTorch
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‎.github/workflows/ci.yml‎

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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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python-version: "3.13"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro, PyTorch
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‎.github/workflows/publish.yml‎

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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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python-version: "3.13"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro, PyTorch
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‎environment.yml‎

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channels:
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- default
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dependencies:
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- python=3.12
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- anaconda=2024.10
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- python=3.13
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- anaconda=2025.06
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- pip
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- pip:
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- jupyter-book==1.0.3
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- quantecon-book-theme==0.7.6
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- sphinx-tojupyter==0.3.0
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- jupyter-book==1.0.4post1
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- quantecon-book-theme==0.9.0
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- sphinx-tojupyter==0.3.1
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- sphinxext-rediraffe==0.2.7
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- sphinx-reredirects==0.1.4
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- sphinx-exercise==1.0.1
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- sphinx-proof==0.2.0
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- ghp-import==1.1.0
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- sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
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- sphinx-proof==0.2.1
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- sphinxcontrib-youtube==1.4.1
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- sphinx-togglebutton==0.3.2
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- sphinx-reredirects==0.1.4
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‎lectures/career.md‎

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In particular, modulo randomness, reproduce the following figure (where the horizontal axis represents time)
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```{figure} /_static/lecture_specific/career/career_solutions_ex1_py.png
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```{image} /_static/lecture_specific/career/career_solutions_ex1_py.png
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:align: center
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```
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```{hint}
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‎lectures/finite_markov.md‎

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To illustrate the idea, consider the following diagram
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```{figure} /_static/lecture_specific/finite_markov/web_graph.png
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```{image} /_static/lecture_specific/finite_markov/web_graph.png
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:align: center
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```
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Imagine that this is a miniature version of the WWW, with
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‎lectures/ifp.md‎

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Reproduce the following figure, which shows (approximately) optimal consumption policies for different interest rates
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```{figure} /_static/lecture_specific/ifp/ifp_policies.png
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```{image} /_static/lecture_specific/ifp/ifp_policies.png
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:align: center
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```
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* Other than `r`, all parameters are at their default values.
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‎lectures/kalman.md‎

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Your figure should -- modulo randomness -- look something like this
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```{figure} /_static/lecture_specific/kalman/kl_ex1_fig.png
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```{image} /_static/lecture_specific/kalman/kl_ex1_fig.png
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:align: center
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```
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```{exercise-end}
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Your figure should show error erratically declining something like this
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```{figure} /_static/lecture_specific/kalman/kl_ex2_fig.png
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```{image} /_static/lecture_specific/kalman/kl_ex2_fig.png
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:align: center
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```
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```{exercise-end}
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You should end up with a figure similar to the following (modulo randomness)
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```{figure} /_static/lecture_specific/kalman/kalman_ex3.png
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```{image} /_static/lecture_specific/kalman/kalman_ex3.png
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:align: center
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```
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Observe how, after an initial learning period, the Kalman filter performs quite well, even relative to the competitor who predicts optimally with knowledge of the latent state.
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‎lectures/markov_perf.md‎

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e1 = e2 = np.array([10, 10, 3])
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```
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```{figure} /_static/lecture_specific/markov_perf/judd_fig2.png
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```{image} /_static/lecture_specific/markov_perf/judd_fig2.png
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Inventories trend to a common steady state.
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This is indeed the case, as the next figure shows
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```{image} /_static/lecture_specific/markov_perf/judd_fig1.png
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In this exercise, reproduce the figure when $\delta = 0.02$.
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‎lectures/ols.md‎

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```{code-cell} python3
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# Load in data
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df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable4.dta?raw=true')
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df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable4.dta')
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```{code-cell} python3
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df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable1.dta?raw=true')
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df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable1.dta')
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df1 = df1.dropna(subset=['logpgp95', 'avexpr'])
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# Add a constant term

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