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[ak_aiyagari] Fix inconsistent household reference (hh → household) #706

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[ak_aiyagari] Fix inconsistent household reference (hh → household) #706

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09d9208
[ak_aiyagari] Refactor household variable references in functions
sudayuga Nov 16, 2025
ab84beb
Merge branch 'main' into ak_aiyagari_patch
HumphreyYang Nov 16, 2025
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18 changes: 9 additions & 9 deletions lectures/ak_aiyagari.md
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Original file line number Diff line number Diff line change
Expand Up @@ -524,8 +524,8 @@ 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):
"Update population distribution from age j to j+1"
Expand Down Expand Up @@ -626,7 +626,7 @@ 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))

# Compute private savings
a = a_grid.reshape((1, a_size, 1))
Expand Down Expand Up @@ -693,13 +693,13 @@ def find_ss(household, firm, pol_target, Q, tol=1e-6, verbose=False):
r_old, w_old, τ_old = r, w, τ

# Household optimal decisions and values
V, σ = backwards_opt([r, w], [τ, δ], hh, Q)
V, σ = backwards_opt([r, w], [τ, δ], household, Q)

# Compute the stationary distribution
μ = popu_dist(σ, hh, Q)
μ = popu_dist(σ, household, Q)

# Compute aggregates
A, L = compute_aggregates(μ, hh)
A, L = compute_aggregates(μ, household)
K = A - D

# Update prices
Expand Down Expand Up @@ -860,8 +860,8 @@ 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 @@ -997,7 +997,7 @@ def path_iteration(ss1, ss2, pol_target, household, firm, Q, tol=1e-4, verbose=F

# Solve optimal policies backwards
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)

# Compute population evolution forwards
μ_seq, K_seq, L_seq = simulate_forwards(
Expand Down
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