def compute_asset_series(cp, T=500000):
"""
Simulates a time series of length T for assets, given optimal savings
behavior. Parameter cp is an instance of consumerProblem
"""
Pi, z_vals, R = cp.Pi, cp.z_vals, cp.R # Simplify names
v_init, c_init = initialize(cp)
c = compute_fixed_point(coleman_operator, cp, c_init)
cf = lambda a, i_z: interp(a, cp.asset_grid, c[:, i_z])
a = np.zeros(T+1)
z_seq = mc_tools.sample_path(Pi, sample_size=T)
for t in range(T):
i_z = z_seq[t]
a[t+1] = R * a[t] + z_vals[i_z] - cf(a[t], i_z)
return a
from compute_fp import compute_fixed_point
from matplotlib import pyplot as plt
import numpy as np
from ifp import coleman_operator, consumerProblem, initialize
r_vals = np.linspace(0, 0.04, 4)
fig, ax = plt.subplots()
for r_val in r_vals:
cp = consumerProblem(r=r_val)
v_init, c_init = initialize(cp)
c = compute_fixed_point(coleman_operator, cp, c_init)
ax.plot(cp.asset_grid, c[:, 0], label=r'$r = %.3f$' % r_val)
ax.set_xlabel('asset level')
ax.set_ylabel('consumption (low income)')
ax.legend(loc='upper left')
plt.show()
from compute_fp import compute_fixed_point
from matplotlib import pyplot as plt
import numpy as np
from ifp import coleman_operator, consumerProblem, initialize
r_vals = np.linspace(0, 0.04, 4)
fig, ax = plt.subplots()
for r_val in r_vals:
cp = consumerProblem(r=r_val)
v_init, c_init = initialize(cp)
c = compute_fixed_point(coleman_operator, cp, c_init)
ax.plot(cp.asset_grid, c[:, 0], label=r'$r = %.3f$' % r_val)
ax.set_xlabel('asset level')
ax.set_ylabel('consumption (low income)')
ax.legend(loc='upper left')
plt.show()
