Merge branch 'main' into i-524

Author: mmcky

.github/workflows/cache.yml

@@ -17,7 +17,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
@@ -53,7 +53,7 @@ jobs:
         uses: actions/upload-artifact@v4
         if: failure()
         with:
-          name: execution-reports
+          name: execution-reports-notebooks
           path: _build/jupyter/reports
       - name: Build PDF from LaTeX
         shell: bash -l {0}

.github/workflows/publish.yml

@@ -16,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch

environment.yml

@@ -2,17 +2,18 @@ name: quantecon
 channels:
   - default
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.7.6
-    - sphinx-tojupyter==0.3.0
+    - jupyter-book==1.0.4post1
+    - quantecon-book-theme==0.9.0
+    - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
-    - sphinx-reredirects==0.1.4
     - sphinx-exercise==1.0.1
-    - sphinx-proof==0.2.0
-    - ghp-import==1.1.0
-    - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
+    - sphinx-proof==0.2.1
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
+    - sphinx-reredirects==0.1.4
+
+

lectures/_config.yml

@@ -37,6 +37,7 @@ sphinx:
     # false-positive links
     linkcheck_ignore: ['https://online.stat.psu.edu/stat415/book/export/html/834']
     bibtex_reference_style: author_year
+    suppress_warnings: ["mystnb.unknown_mime_type"]
     nb_mime_priority_overrides: [
        # HTML
        ['html', 'application/vnd.jupyter.widget-view+json', 10],
@@ -97,6 +98,9 @@ sphinx:
       advanced:
         - "https://python-advanced.quantecon.org"
         - null
+      jax:
+        - "https://jax.quantecon.org/"
+        - null
     mathjax3_config:
       tex:
         macros:

lectures/_static/quant-econ.bib

@@ -3,6 +3,16 @@
 Note: Extended Information (like abstracts, doi, url's etc.) can be found in quant-econ-extendedinfo.bib file in _static/
 ###
 
+@article{blume2018case,
+  title={A case for incomplete markets},
+  author={Blume, Lawrence E and Cogley, Timothy and Easley, David A and Sargent, Thomas J and Tsyrennikov, Viktor},
+  journal={Journal of Economic Theory},
+  volume={178},
+  pages={191--221},
+  year={2018},
+  publisher={Elsevier}
+}
+
 @article{shannon1948mathematical,
   title={A mathematical theory of communication},
   author={Shannon, Claude E},

lectures/aiyagari.md

@@ -57,7 +57,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.markov import DiscreteDP
 from numba import jit

lectures/back_prop.md

@@ -16,25 +16,17 @@ kernelspec:
 ```{include} _admonition/gpu.md
 ```
 
-```{code-cell} ipython3
-:tags: [skip-execution]
-
-!pip install --upgrade jax
-```
-
-```{code-cell} ipython3
-import jax
-## to check that gpu is activated in environment
-print(f"JAX backend: {jax.devices()[0].platform}")
-```
-
 In addition to what's included in base Anaconda, we need to install the following packages
 
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install kaleido
-!conda install -y -c plotly plotly plotly-orca retrying
+!pip install -U kaleido plotly
+!conda install -y -c plotly plotly-orca
+
+# kaleido needs chrome to build images
+import kaleido
+kaleido.get_chrome_sync()
 ```
 
 ```{note}
@@ -44,6 +36,19 @@ the Python packages. However this lecture will still execute as Google Colab
 has `plotly` installed.
 ```
 
+We also need to install JAX to run this lecture
+
+```{code-cell} ipython3
+:tags: [skip-execution]
+
+!pip install --upgrade jax
+```
+
+```{code-cell} ipython3
+import jax
+print(f"JAX backend: {jax.devices()[0].platform}") # to check that gpu is activated in environment
+```
+
 ## Overview
 
 Substantial parts of **machine learning** and **artificial intelligence** are about 

lectures/cake_eating_problem.md

@@ -41,11 +41,10 @@ In what follows, we require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 
-## The Model
+## The model
 
 We consider an infinite time horizon $t=0, 1, 2, 3..$
 
@@ -115,7 +114,7 @@ In this problem, the following terminology is standard:
 * $c_t$ is called the **control variable** or the **action**
 * $\beta$ and $\gamma$ are **parameters**
 
-### Trade-Off
+### Trade-off
 
 The key trade-off in the cake-eating problem is this:
 
@@ -145,14 +144,14 @@ parameters*.
 
 Let's see if this is true.
 
-## The Value Function
+## The value function
 
 The first step of our dynamic programming treatment is to obtain the Bellman
 equation.
 
 The next step is to use it to calculate the solution.
 
-### The Bellman Equation
+### The Bellman equation
 
 To this end, we let $v(x)$ be maximum lifetime utility attainable from
 the current time when $x$ units of cake are left.
@@ -199,7 +198,7 @@ If $c$ is chosen optimally using this trade off strategy, then we obtain maximal
 
 Hence, $v(x)$ equals the right hand side of {eq}`bellman-cep`, as claimed.
 
-### An Analytical Solution
+### An analytical solution
 
 It has been shown that, with $u$ as the CRRA utility function in
 {eq}`crra_utility`, the function
@@ -249,7 +248,7 @@ ax.legend(fontsize=12)
 plt.show()
 ```
 
-## The Optimal Policy
+## The optimal policy
 
 Now that we have the value function, it is straightforward to calculate the
 optimal action at each state.
@@ -309,7 +308,7 @@ ax.legend()
 plt.show()
 ```
 
-## The Euler Equation
+## The Euler equation
 
 In the discussion above we have provided a complete solution to the cake
 eating problem in the case of CRRA utility.
@@ -323,7 +322,7 @@ Euler equation.
 This is because, for more difficult problems, this equation
 provides key insights that are hard to obtain by other methods.
 
-### Statement and Implications
+### Statement and implications
 
 The Euler equation for the present problem can be stated as
 
@@ -376,7 +375,7 @@ see proposition 2.2 of {cite}`ma2020income`.
 The following arguments focus on necessity, explaining why an optimal path or
 policy should satisfy the Euler equation.
 
-### Derivation I: A Perturbation Approach
+### Derivation I: a perturbation approach
 
 Let's write $c$ as a shorthand for consumption path $\{c_t\}_{t=0}^\infty$.
 
@@ -444,7 +443,7 @@ $$
 
 This is just the Euler equation.
 
-### Derivation II: Using the Bellman Equation
+### Derivation II: using the Bellman equation
 
 Another way to derive the Euler equation is to use the Bellman equation {eq}`bellman-cep`.
 

lectures/career.md

@@ -48,7 +48,6 @@ We begin with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numba import jit, prange
@@ -370,7 +369,8 @@ when the worker follows the optimal policy.
 
 In particular, modulo randomness, reproduce the following figure (where the horizontal axis represents time)
 
-```{figure} /_static/lecture_specific/career/career_solutions_ex1_py.png
+```{image} /_static/lecture_specific/career/career_solutions_ex1_py.png
+:align: center
 ```
 
 ```{hint}