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Make main build again: install JAX in the environment, finish ak_aiyagari's household rename, add the programming intersphinx key #297

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Merged
mmcky merged 3 commits into from
Oct 18, 2026
Merged

Make main build again: install JAX in the environment, finish ak_aiyagari's household rename, add the programming intersphinx key #297

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7cd9274
ak_aiyagari: finish the hh → household rename in the six cells the 20…
mmcky Oct 18, 2026
40d0eb1
_config: map the programming intersphinx key to the zh-cn programming…
mmcky Oct 18, 2026
ffa45ad
environment: install jax 0.11.0, since two lectures skip their own in…
mmcky Oct 18, 2026
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6 changes: 4 additions & 2 deletions environment.yml
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Expand Up @@ -15,5 +15,7 @@ dependencies:
- sphinxcontrib-youtube==1.5.0
- sphinx-togglebutton==0.4.5
- sphinx-reredirects==1.1.0


# Pinned: jax 0.11.1 hangs lax.fori_loop on the CPU path (QuantEcon/workspace-lectures#49),
# and two lectures (ak_aiyagari, two_computation) skip their own `!pip install jax` cell
# because the English runner preinstalls JAX; this CI has to as well.
- jax==0.11.0
3 changes: 3 additions & 0 deletions lectures/_config.yml
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Expand Up @@ -130,6 +130,9 @@ sphinx:
jax:
- "https://jax.quantecon.org/"
- null
programming:
- "https://quantecon.github.io/lecture-python-programming.zh-cn/"
- null
mathjax3_config:
tex:
macros:
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18 changes: 9 additions & 9 deletions lectures/ak_aiyagari.md
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Expand Up @@ -575,8 +575,8 @@ def popu_dist(σ, household, Q):

j_grid, a_grid, γ_grid, Π, β, init_μ, VJ = household

J = hh.j_grid.size
num_state = hh.a_grid.size * hh.γ_grid.size
J = household.j_grid.size
num_state = household.a_grid.size * household.γ_grid.size

def update_popu_j(μ_j, j):
"更新从年龄j到j+1的人口分布"
Expand Down Expand Up @@ -691,7 +691,7 @@ def compute_aggregates(μ, household):

J, a_size, γ_size = j_grid.size, a_grid.size, γ_grid.size

μ = μ.reshape((J, hh.a_grid.size, hh.γ_grid.size))
μ = μ.reshape((J, household.a_grid.size, household.γ_grid.size))

# 计算私人储蓄
a = a_grid.reshape((1, a_size, 1))
Expand Down Expand Up @@ -758,13 +758,13 @@ def find_ss(household, firm, pol_target, Q, tol=1e-6, max_iter=200):
r_old, w_old = r, w

# 家庭最优决策和价值
V, σ = backwards_opt([r, w], [τ, δ], hh, Q)
V, σ = backwards_opt([r, w], [τ, δ], household, Q)

# 计算稳态分布
μ = popu_dist(σ, hh, Q)
μ = popu_dist(σ, household, Q)

# 计算总量
A, L = compute_aggregates(μ, hh)
A, L = compute_aggregates(μ, household)
K = A - D

# 更新价格
Expand Down Expand Up @@ -928,8 +928,8 @@ def population_evolution(σt, μt, household, Q):

j_grid, a_grid, γ_grid, Π, β, init_μ, VJ = household

J = hh.j_grid.size
num_state = hh.a_grid.size * hh.γ_grid.size
J = household.j_grid.size
num_state = household.a_grid.size * household.γ_grid.size

def population_evolution_j(j):

Expand Down Expand Up @@ -1066,7 +1066,7 @@ def path_iteration(ss1, ss2, pol_target, household, firm, Q, tol=1e-4,

# 向后求解最优政策
V_seq, σ_seq = solve_backwards(
V_ss2, σ_ss2, hh, firm, price_seq, pol_seq, Q)
V_ss2, σ_ss2, household, firm, price_seq, pol_seq, Q)

# 向前计算人口演变
μ_seq, K_seq, L_seq = simulate_forwards(
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