ENH: Migrate to sphinx-exercise

lectures/_config.yml

@@ -32,7 +32,7 @@ latex:
       targetname: quantecon-python.tex
 
 sphinx:
-  extra_extensions: [sphinx_multitoc_numbering, sphinxext.rediraffe, sphinx_tojupyter, sphinxcontrib.youtube, sphinx.ext.todo]
+  extra_extensions: [sphinx_multitoc_numbering, sphinxext.rediraffe, sphinx_tojupyter, sphinxcontrib.youtube, sphinx.ext.todo, sphinx_exercise, sphinx_togglebutton]
   config:
     nb_render_priority:
       html:

lectures/ar1_processes.md

@@ -322,7 +322,8 @@ important concept for statistics and simulation.
 
 ## Exercises
 
-### Exercise 1
+```{exercise}
+:label: ar1p_ex1
 
 Let $k$ be a natural number.
 
@@ -355,8 +356,11 @@ $$
 when $m$ is large.
 
 Confirm this by simulation at a range of $k$ using the default parameters from the lecture.
+```
+
 
-### Exercise 2
+```{exercise}
+:label: ar1p_ex2
 
 Write your own version of a one dimensional [kernel density
 estimator](https://en.wikipedia.org/wiki/Kernel_density_estimation),
@@ -398,8 +402,11 @@ Use $n=500$.
 
 Make a comment on your results. (Do you think this is a good estimator
 of these distributions?)
+```
+
 
-### Exercise 3
+```{exercise}
+:label: ar1p_ex3
 
 In the lecture we discussed the following fact: for the $AR(1)$ process
 
@@ -438,10 +445,14 @@ color) as follows:
 Try this for $n=2000$ and confirm that the
 simulation based estimate of $\psi_{t+1}$ does converge to the
 theoretical distribution.
+```
+
 
 ## Solutions
 
-### Exercise 1
+```{solution-start} ar1p_ex1
+:class: dropdown
+```
 
 ```{code-cell} python3
 from numba import njit
@@ -479,7 +490,13 @@ ax.legend()
 plt.show()
 ```
 
-### Exercise 2
+```{solution-end}
+```
+
+
+```{solution-start} ar1p_ex2
+:class: dropdown
+```
 
 Here is one solution:
 
@@ -532,7 +549,13 @@ for α, β in parameter_pairs:
 We see that the kernel density estimator is effective when the underlying
 distribution is smooth but less so otherwise.
 
-### Exercise 3
+```{solution-end}
+```
+
+
+```{solution-start} ar1p_ex3
+:class: dropdown
+```
 
 Here is our solution
 
@@ -579,3 +602,5 @@ plt.show()
 The simulated distribution approximately coincides with the theoretical
 distribution, as predicted.
 
+```{solution-end}
+```

lectures/cake_eating_numerical.md

@@ -482,7 +482,8 @@ This is due to
 
 ## Exercises
 
-### Exercise 1
+```{exercise}
+:label: cen_ex1
 
 Try the following modification of the problem.
 
@@ -500,15 +501,22 @@ where $\alpha$ is a parameter satisfying $0 < \alpha < 1$.
 Make the required changes to value function iteration code and plot the value and policy functions.
 
 Try to reuse as much code as possible.
+```
+
 
-### Exercise 2
+```{exercise}
+:label: cen_ex2
 
 Implement time iteration, returning to the original case (i.e., dropping the
 modification in the exercise above).
+```
+
 
 ## Solutions
 
-### Exercise 1
+```{solution-start} cen_ex1
+:class: dropdown
+```
 
 We need to create a class to hold our primitives and return the right hand side of the Bellman equation.
 
@@ -582,7 +590,13 @@ plt.show()
 
 Consumption is higher when $\alpha < 1$ because, at least for large $x$, the return to savings is lower.
 
-### Exercise 2
+```{solution-end}
+```
+
+
+```{solution-start} cen_ex2
+:class: dropdown
+```
 
 Here's one way to implement time iteration.
 
@@ -670,3 +684,5 @@ ax.legend(fontsize=12)
 plt.show()
 ```
 
+```{solution-end}
+```

lectures/cake_eating_problem.md

@@ -506,7 +506,8 @@ Combining this fact with {eq}`bellman_envelope` recovers the Euler equation.
 
 ## Exercises
 
-### Exercise 1
+```{exercise}
+:label: cep_ex1
 
 How does one obtain the expressions for the value function and optimal policy
 given in {eq}`crra_vstar` and {eq}`crra_opt_pol` respectively?
@@ -523,10 +524,12 @@ Starting from this conjecture, try to obtain the solutions {eq}`crra_vstar` and
 
 In doing so, you will need to use the definition of the value function and the
 Bellman equation.
+```
 
 ## Solutions
 
-### Exercise 1
+```{solution} cep_ex1
+:class: dropdown
 
 We start with the conjecture $c_t^*=\theta x_t$, which leads to a path
 for the state variable (cake size) given by
@@ -611,4 +614,4 @@ v^*(x_t) = \left(1-\beta^\frac{1}{\gamma}\right)^{-\gamma}u(x_t)
 $$
 
 Our claims are now verified.
-
+```

lectures/career.md

@@ -362,8 +362,9 @@ the worker cannot change careers without changing jobs.
 
 ## Exercises
 
-(career_ex1)=
-### Exercise 1
+```{exercise-start}
+:label: career_ex1
+```
 
 Using the default parameterization in the class `CareerWorkerProblem`,
 generate and plot typical sample paths for $\theta$ and $\epsilon$
@@ -372,13 +373,16 @@ 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
-
 ```
 
 Hint: To generate the draws from the distributions $F$ and $G$, use `quantecon.random.draw()`.
 
-(career_ex2)=
-### Exercise 2
+```{exercise-end}
+```
+
+
+```{exercise}
+:label: career_ex2
 
 Let's now consider how long it takes for the worker to settle down to a
 permanent job, given a starting point of $(\theta, \epsilon) = (0, 0)$.
@@ -402,16 +406,21 @@ $$
 Collect 25,000 draws of this random variable and compute the median (which should be about 7).
 
 Repeat the exercise with $\beta=0.99$ and interpret the change.
+```
+
 
-(career_ex3)=
-### Exercise 3
+```{exercise}
+:label: career_ex3
 
 Set the parameterization to `G_a = G_b = 100` and generate a new optimal policy
 figure -- interpret.
+```
 
 ## Solutions
 
-### Exercise 1
+```{solution-start} career_ex1
+:class: dropdown
+```
 
 Simulate job/career paths.
 
@@ -455,7 +464,13 @@ plt.legend()
 plt.show()
 ```
 
-### Exercise 2
+```{solution-end}
+```
+
+
+```{solution-start} career_ex2
+:class: dropdown
+```
 
 The median for the original parameterization can be computed as follows
 
@@ -498,7 +513,13 @@ The medians are subject to randomness but should be about 7 and 14 respectively.
 
 Not surprisingly, more patient workers will wait longer to settle down to their final job.
 
-### Exercise 3
+```{solution-end}
+```
+
+
+```{solution-start} career_ex3
+:class: dropdown
+```
 
 ```{code-cell} python3
 cw = CareerWorkerProblem(G_a=100, G_b=100)
@@ -522,3 +543,6 @@ In the new figure, you see that the region for which the worker
 stays put has grown because the distribution for $\epsilon$
 has become more concentrated around the mean, making high-paying jobs
 less realistic.
+
+```{solution-end}
+```

lectures/cass_koopmans_1.md

@@ -866,17 +866,28 @@ state in which $f'(K)=\rho +\delta$.
 
 ### Exercise
 
+```{exercise}
+:label: ck1_ex1
+
 - Plot the optimal consumption, capital, and saving paths when the
   initial capital level begins at 1.5 times the steady state level
   as we shoot towards the steady state at $T=130$.
 - Why does the saving rate respond as it does?
+```
 
 ### Solution
 
+```{solution-start} ck1_ex1
+:class: dropdown
+```
+
 ```{code-cell} python3
 plot_saving_rate(pp, 0.3, k_ss*1.5, [130], k_ter=k_ss, k_ss=k_ss, s_ss=s_ss)
 ```
 
+```{solution-end}
+```
+
 ## Concluding Remarks
 
 In {doc}`Cass-Koopmans Competitive Equilibrium <cass_koopmans_2>`,  we study a decentralized version of an economy with exactly the same

lectures/coleman_policy_iter.md

@@ -424,7 +424,8 @@ and accuracy, at least for this model.
 
 ## Exercises
 
-### Exercise 1
+```{exercise}
+:label: cpi_ex1
 
 Solve the model with CRRA utility
 
@@ -435,10 +436,13 @@ $$
 Set `γ = 1.5`.
 
 Compute and plot the optimal policy.
+```
 
 ## Solutions
 
-### Exercise 1
+```{solution-start} cpi_ex1
+:class: dropdown
+```
 
 We use the class `OptimalGrowthModel_CRRA` from our {doc}`VFI lecture <optgrowth_fast>`.
 
@@ -468,3 +472,5 @@ ax.legend()
 plt.show()
 ```
 
+```{solution-end}
+```