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[wealth_dynamics.md] Update np.random → Generator API #989

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jstac merged 1 commit into from
Jul 21, 2026
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[wealth_dynamics.md] Update np.random → Generator API #989

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10 changes: 6 additions & 4 deletions lectures/wealth_dynamics.md
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Original file line number Diff line number Diff line change
Expand Up @@ -96,8 +96,9 @@ The package [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py), already i
To illustrate, suppose that

```{code-cell} ipython3
rng = np.random.default_rng()
n = 10_000 # size of sample
w = np.exp(np.random.randn(n)) # lognormal draws
w = np.exp(rng.standard_normal(n)) # lognormal draws
```

is data representing the wealth of 10,000 households.
Expand Down Expand Up @@ -137,7 +138,7 @@ a_vals = (1, 2, 5) # Pareto tail index
n = 10_000 # size of each sample
fig, ax = plt.subplots()
for a in a_vals:
u = np.random.uniform(size=n)
u = rng.uniform(size=n)
y = u**(-1/a) # distributed as Pareto with tail index a
f_vals, l_vals = qe.lorenz_curve(y)
ax.plot(f_vals, l_vals, label=f'$a = {a}$')
Expand Down Expand Up @@ -177,7 +178,7 @@ n = 100

fig, ax = plt.subplots()
for a in a_vals:
y = np.random.weibull(a, size=n)
y = rng.weibull(a, size=n)
ginis.append(qe.gini_coefficient(y))
ginis_theoretical.append(1 - 2**(-1/a))
ax.plot(a_vals, ginis, label='estimated gini coefficient')
Expand Down Expand Up @@ -561,13 +562,14 @@ Here is one solution, which produces a good match between theory and
simulation.

```{code-cell} ipython3
rng = np.random.default_rng()
a_vals = np.linspace(1, 10, 25) # Pareto tail index
ginis = np.empty_like(a_vals)

n = 1000 # size of each sample
fig, ax = plt.subplots()
for i, a in enumerate(a_vals):
y = np.random.uniform(size=n)**(-1/a)
y = rng.uniform(size=n)**(-1/a)
ginis[i] = qe.gini_coefficient(y)
ax.plot(a_vals, ginis, label='sampled')
ax.plot(a_vals, 1/(2*a_vals - 1), label='theoretical')
Expand Down
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