Merge branch 'main' into preview-theme-335

Author: mmcky

.github/workflows/cache.yml

@@ -8,7 +8,7 @@ jobs:
   cache:
     runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: ${{ github.event.pull_request.head.sha }}
       - name: Setup Anaconda

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ jobs:
       NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
       NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: ${{ github.event.pull_request.head.sha }}
           fetch-depth: 0

.github/workflows/collab.yml

@@ -13,7 +13,7 @@ jobs:
       image: docker://us-docker.pkg.dev/colab-images/public/runtime:latest
       options: --gpus all
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       # Install build software
       - name: Install Build Software & LaTeX
         shell: bash -l {0}

.github/workflows/linkcheck.yml

@@ -13,7 +13,7 @@ jobs:
     steps:
       # Checkout the live site (html)
       - name: Checkout
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: gh-pages
       - name: Link Checker

.github/workflows/publish.yml

@@ -9,7 +9,7 @@ jobs:
     runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
     steps:
       - name: Checkout
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
       - name: Setup Anaconda

lectures/ifp_discrete.md

@@ -168,7 +168,7 @@ def create_consumption_model(
         β=0.98,                    # Discount factor
         γ=2,                       # CRRA parameter
         a_min=0.01,                # Min assets
-        a_max=5.0,                 # Max assets
+        a_max=10.0,                # Max assets
         a_size=150,                # Grid size
         ρ=0.9, ν=0.1, y_size=100   # Income parameters
     ):
@@ -348,6 +348,39 @@ print(f"Relative speed = {python_time / jax_without_compile:.2f}")
 ```
 
 
+### Asset Dynamics
+
+To understand long-run behavior, let's examine the asset accumulation dynamics under the optimal policy.
+
+The following 45-degree diagram shows how assets evolve over time:
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+# Plot asset accumulation for first and last income states
+for j, label in zip([0, -1], ['low income', 'high income']):
+    # Get next-period assets for each current asset level
+    a_next = model.a_grid[σ_star_jax[:, j]]
+    ax.plot(model.a_grid, a_next, label=label)
+
+# Add 45-degree line
+ax.plot(model.a_grid, model.a_grid, 'k--', linewidth=0.5)
+ax.set(xlabel='current assets', ylabel='next period assets')
+ax.legend()
+plt.show()
+```
+
+The plot shows the asset accumulation rule for each income state.
+
+The dotted line is the 45-degree line, representing points where $a_{t+1} = a_t$.
+
+We see that:
+
+* For low income levels, assets tend to decrease (points below the 45-degree line)
+* For high income levels, assets tend to increase at low asset levels
+* The dynamics suggest convergence to a stationary distribution
+
+
 ## Exercises
 
 ```{exercise}

lectures/ifp_opi.md

@@ -88,7 +88,7 @@ def create_consumption_model(
         β=0.98,                    # Discount factor
         γ=2,                       # CRRA parameter
         a_min=0.01,                # Min assets
-        a_max=5.0,                 # Max assets
+        a_max=10.0,                # Max assets
         a_size=150,                # Grid size
         ρ=0.9, ν=0.1, y_size=100   # Income parameters
     ):
@@ -109,52 +109,50 @@ We repeat some functions from {doc}`ifp_discrete`.
 Here is the right hand side of the Bellman equation:
 
 ```{code-cell} ipython3
-@jax.jit
-def B(v, model):
+def B(v, model, i, j, ip):
     """
-    A vectorized version of the right-hand side of the Bellman equation
-    (before maximization), which is a 3D array representing
+    The right-hand side of the Bellman equation before maximization, which takes
+    the form
 
         B(a, y, a′) = u(Ra + y - a′) + β Σ_y′ v(a′, y′) Q(y, y′)
 
-    for all (a, y, a′).
+    The indices are (i, j, ip) -> (a, y, a′).
     """
-
-    # Unpack
     β, R, γ, a_grid, y_grid, Q = model
-    a_size, y_size = len(a_grid), len(y_grid)
-
-    # Compute current rewards r(a, y, ap) as array r[i, j, ip]
-    a  = jnp.reshape(a_grid, (a_size, 1, 1))    # a[i]   ->  a[i, j, ip]
-    y  = jnp.reshape(y_grid, (1, y_size, 1))    # z[j]   ->  z[i, j, ip]
-    ap = jnp.reshape(a_grid, (1, 1, a_size))    # ap[ip] -> ap[i, j, ip]
+    a, y, ap  = a_grid[i], y_grid[j], a_grid[ip]
     c = R * a + y - ap
+    EV = jnp.sum(v[ip, :] * Q[j, :])
+    return jnp.where(c > 0, c**(1-γ)/(1-γ) + β * EV, -jnp.inf)
+```
 
-    # Calculate continuation rewards at all combinations of (a, y, ap)
-    v = jnp.reshape(v, (1, 1, a_size, y_size))  # v[ip, jp] -> v[i, j, ip, jp]
-    Q = jnp.reshape(Q, (1, y_size, 1, y_size))  # Q[j, jp]  -> Q[i, j, ip, jp]
-    EV = jnp.sum(v * Q, axis=3)                 # sum over last index jp
+Now we successively apply `vmap` to vectorize over all indices:
 
-    # Compute the right-hand side of the Bellman equation
-    return jnp.where(c > 0, c**(1-γ)/(1-γ) + β * EV, -jnp.inf)
+```{code-cell} ipython3
+B_1    = jax.vmap(B,   in_axes=(None, None, None, None, 0))
+B_2    = jax.vmap(B_1, in_axes=(None, None, None, 0,    None))
+B_vmap = jax.vmap(B_2, in_axes=(None, None, 0,    None, None))
 ```
 
 Here's the Bellman operator:
 
 ```{code-cell} ipython3
-@jax.jit
 def T(v, model):
     "The Bellman operator."
-    return jnp.max(B(v, model), axis=2)
+    a_indices = jnp.arange(len(model.a_grid))
+    y_indices = jnp.arange(len(model.y_grid))
+    B_values = B_vmap(v, model, a_indices, y_indices, a_indices)
+    return jnp.max(B_values, axis=-1)
 ```
 
 Here's the function that computes a $v$-greedy policy:
 
 ```{code-cell} ipython3
-@jax.jit
 def get_greedy(v, model):
     "Computes a v-greedy policy, returned as a set of indices."
-    return jnp.argmax(B(v, model), axis=2)
+    a_indices = jnp.arange(len(model.a_grid))
+    y_indices = jnp.arange(len(model.y_grid))
+    B_values = B_vmap(v, model, a_indices, y_indices, a_indices)
+    return jnp.argmax(B_values, axis=-1)
 ```
 
 Now we define the policy operator $T_\sigma$, which is the Bellman operator with
@@ -194,7 +192,6 @@ Apply vmap to vectorize:
 T_σ_1    = jax.vmap(T_σ,   in_axes=(None, None, None, None, 0))
 T_σ_vmap = jax.vmap(T_σ_1, in_axes=(None, None, None, 0,    None))
 
-@jax.jit
 def T_σ_vec(v, σ, model):
     """Vectorized version of T_σ."""
     a_size, y_size = len(model.a_grid), len(model.y_grid)
@@ -206,7 +203,6 @@ def T_σ_vec(v, σ, model):
 Now we need a function to apply the policy operator m times:
 
 ```{code-cell} ipython3
-@jax.jit
 def iterate_policy_operator(σ, v, m, model):
     """
     Apply the policy operator T_σ exactly m times to v.
@@ -324,9 +320,9 @@ print(f"VFI completed in {vfi_time:.2f} seconds.")
 Now let's time OPI with different values of m:
 
 ```{code-cell} ipython3
-print("Starting OPI with m=10.")
+print("Starting OPI with m=50.")
 start = time()
-v_star_opi, σ_star_opi = optimistic_policy_iteration(model, m=10)
+v_star_opi, σ_star_opi = optimistic_policy_iteration(model, m=50)
 v_star_opi.block_until_ready()
 opi_time_with_compile = time() - start
 print(f"OPI completed in {opi_time_with_compile:.2f} seconds.")
@@ -336,7 +332,7 @@ Run it again:
 
 ```{code-cell} ipython3
 start = time()
-v_star_opi, σ_star_opi = optimistic_policy_iteration(model, m=10)
+v_star_opi, σ_star_opi = optimistic_policy_iteration(model, m=50)
 v_star_opi.block_until_ready()
 opi_time = time() - start
 print(f"OPI completed in {opi_time:.2f} seconds.")
@@ -345,9 +341,38 @@ print(f"OPI completed in {opi_time:.2f} seconds.")
 Check that we get the same result:
 
 ```{code-cell} ipython3
-print(f"Policies match: {jnp.allclose(σ_star_vfi, σ_star_opi)}")
+print(f"Values match: {jnp.allclose(v_star_vfi, v_star_opi)}")
 ```
 
+The value functions match, confirming both algorithms converge to the same solution.
+
+Let's visually compare the asset dynamics under both policies:
+
+```{code-cell} ipython3
+fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+
+# VFI policy
+for j, label in zip([0, -1], ['low income', 'high income']):
+    a_next_vfi = model.a_grid[σ_star_vfi[:, j]]
+    axes[0].plot(model.a_grid, a_next_vfi, label=label)
+axes[0].plot(model.a_grid, model.a_grid, 'k--', linewidth=0.5, alpha=0.5)
+axes[0].set(xlabel='current assets', ylabel='next period assets', title='VFI')
+axes[0].legend()
+
+# OPI policy
+for j, label in zip([0, -1], ['low income', 'high income']):
+    a_next_opi = model.a_grid[σ_star_opi[:, j]]
+    axes[1].plot(model.a_grid, a_next_opi, label=label)
+axes[1].plot(model.a_grid, model.a_grid, 'k--', linewidth=0.5, alpha=0.5)
+axes[1].set(xlabel='current assets', ylabel='next period assets', title='OPI')
+axes[1].legend()
+
+plt.tight_layout()
+plt.show()
+```
+
+The policies are visually indistinguishable, confirming both methods produce the same solution.
+
 Here's the speedup:
 
 ```{code-cell} ipython3
@@ -384,9 +409,7 @@ plt.show()
 
 Here's a summary of the results
 
-* When $m=1$, OPI is slight slower than VFI, even though they should be mathematically equivalent, due to small inefficiencies associated with extra function calls.
-
-* OPI outperforms VFI for a very large range of $m$ values.
+* OPI outperforms VFI for a large range of $m$ values.
 
 * For very large $m$, OPI performance begins to degrade as we spend too much
   time iterating the policy operator.