Execution Error in opt_tax_recur.md and amss Lectures

[mmcky]

There is a reported execution error in opt_tax_recur.md

in this cell

nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
π = np.array([[0, 1, 0,   0,   0,  0],
              [0, 0, 1,   0,   0,  0],
              [0, 0, 0, 0.5, 0.5,  0],
              [0, 0, 0,   0,   0,  1],
              [0, 0, 0,   0,   0,  1],
              [0, 0, 0,   0,   0,  1]])

g = np.array([0.1, 0.1, 0.1, 0.2, 0.1, 0.1])
crra_pref = CRRAutility()

# Solve sequential problem
seq = SequentialLS(crra_pref, π=π, g=g)
sHist_h = np.array([0, 1, 2, 3, 5, 5, 5])
sHist_l = np.array([0, 1, 2, 4, 5, 5, 5])
sim_seq_h = seq.simulate(1, 0, 7, sHist_h)
sim_seq_l = seq.simulate(1, 0, 7, sHist_l)

fig, axes = plt.subplots(3, 2, figsize=(14, 10))
titles = ['Consumption', 'Labor Supply', 'Government Debt',
          'Tax Rate', 'Government Spending', 'Output']

for ax, title, sim_l, sim_h in zip(axes.flatten(),
                                   titles,
                                   sim_seq_l[:6],
                                   sim_seq_h[:6]):
    ax.set(title=title)
    ax.plot(sim_l, '-ok', sim_h, '-or', alpha=0.7)
    ax.grid()

plt.tight_layout()
plt.show()
------------------

results in

ValueError: setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 1.

[HumphreyYang]

Many thanks @mmcky,

I was about to open an issue on this. The same error also appears in:

amss.md
amss2.md
amss3.md

as I am testing the migrated lecture.

They appeared in:

amss.md:

# WARNING: DO NOT EXPECT THE CODE TO WORK IF YOU CHANGE PARAMETERS
s_hist_h = np.array([0, 1, 2, 3, 5, 5, 5])
s_hist_l = np.array([0, 1, 2, 4, 5, 5, 5])

sim_h_amss = amss_model.simulate(s_hist_h, b_0)
sim_l_amss = amss_model.simulate(s_hist_l, b_0)

sim_h_ls = ls_model.simulate(b_0, 0, 7, s_hist_h)
sim_l_ls = ls_model.simulate(b_0, 0, 7, s_hist_l)

fig, axes = plt.subplots(3, 2, figsize=(14, 10))
titles = ['Consumption', 'Labor Supply', 'Government Debt',
          'Tax Rate', 'Government Spending', 'Output']

for ax, title, ls_l, ls_h, amss_l, amss_h in zip(axes.flatten(), titles,
                                                 sim_l_ls, sim_h_ls,
                                                 sim_l_amss, sim_h_amss):
    ax.plot(ls_l, '-ok', ls_h, '-^k', amss_l, '-or', amss_h, '-^r',
            alpha=0.7)
    ax.set(title=title)
    ax.grid()

plt.tight_layout()
plt.show()

amss2.md:

c0, b0 = fsolve(solve_cb, np.array([1., -1.], dtype='float64'),
                args=(Φ_star, b[0], 1), xtol=1.0e-12)
c0, b0

amss3.md:

μ_grid = np.linspace(-0.09, 0.1, 100)

log_example = CRRAutility(π=np.full((3, 3), 1 / 3),
                          G=np.array([0.1, 0.2, .3]),
                          Θ=np.ones(3))

log_example.transfers = True        # Government can use transfers
log_sequential = SequentialAllocation(log_example)  # Solve sequential problem
log_bellman = RecursiveAllocationAMSS(log_example, μ_grid,
                                       tol=1e-12, tol_diff=1e-10)

T = 102000  # Set T to 102000 periods

sim_seq_long = log_sequential.simulate(0.5, 0, T)
sHist_long = sim_seq_long[-3]
sim_bel_long = log_bellman.simulate(0.5, 0, T, sHist_long)

titles = ['Government Debt', 'Tax Rate']

fig, axes = plt.subplots(2, 1, figsize=(10, 8))

for ax, title, id in zip(axes.flatten(), titles, [2, 3]):
    ax.plot(sim_seq_long[id], '-k', sim_bel_long[id], '-.b', alpha=0.5)
    ax.set(title=title)
    ax.grid()

axes[0].legend(('Complete Markets', 'Incomplete Markets'))
plt.tight_layout()
plt.show()

and are related to this deprecation warning before:

/tmp/ipykernel_11213/2711255625.py:130: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.

[mmcky]

Thanks @HumphreyYang feel free to jump in and take ownership of the issue if you know how to fix them.

[HumphreyYang]

Many thanks @mmcky and @Smit-create,

I worked on this when I was building the lecture, so I will take ownership of this issue : )