bins = []
for w, eq in eq2:
bins.append(w * sum(s.ravel() * eq.μ.sum(axis=0)))
from dolark.shocks import discretize_idiosyncratic_shocks
dist = discretize_idiosyncratic_shocks(hmodel2.distribution)
plt.plot([e[1]["β"] for e in dist], bins, "-o")
plt.xlabel("β")
# %%
# Steady-state equilibrium: graphic resolution
from tqdm import tqdm
# %%
# Aiyagari
dr0 = hmodel1.get_starting_rule()
m0 = hmodel1.calibration["exogenous"]
kvec = np.linspace(30, 50, 20)
hmodel1.model.set_calibration({})
eqs = [
equilibrium(hmodel1, m0, np.array([k]), dr0=dr0, return_equilibrium=False)
for k in tqdm(kvec)
]
eqs = [e[0] for e in eqs]
# %%
from matplotlib import pyplot as plt
plt.plot(kvec, kvec - eqs, color="black")
# %%
plt.plot([e[1]['β'] for e in dist], bins, '-o')
plt.xlabel('β')
y0 = eqss[0][1].y
#%%
# by hand
from dolark.shocks import discretize_idiosyncratic_shocks
dist = discretize_idiosyncratic_shocks(hmodel2.distribution)
from tqdm import tqdm
dr0 = hmodel2.get_starting_rule()
m0 = hmodel2.calibration['exogenous']
kvec = np.linspace(20, 40, 20)
eqs = []
for w, kwargs in tqdm(dist):
hmodel2.model.set_calibration(**kwargs)
res = [
equilibrium(hmodel2,
m0,
np.array([k]),
dr0=dr0,
return_equilibrium=False) for k in kvec
]
eqs.append(res)