if ExplicitExample.vFuncBool:
pGrid = np.linspace(0.1, 3.0, 24)
M = np.linspace(0.001, 5, 300)
for p in pGrid:
M_temp = M + ExplicitExample.solution[0].mLvlMin(p)
C = ExplicitExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp))
plt.plot(M_temp, C)
plt.ylim([-200, 0])
plt.xlabel('Market resource level mLvl')
plt.ylabel('Value v')
plt.show()
# %%
# Simulate many periods to get to the stationary distribution
ExplicitExample.T_sim = 500
ExplicitExample.track_vars = ['mLvl', 'cLvl', 'pLvl']
ExplicitExample.initialize_sim()
ExplicitExample.simulate()
plt.plot(np.mean(ExplicitExample.history['mLvl'], axis=1))
plt.xlabel('Simulated time period')
plt.ylabel('Average market resources mLvl')
plt.show()
# %% [markdown]
# ## 2. Persistent income shock consumer
#
#
# Class to solve consumption-saving models with idiosyncratic shocks to income in which shocks are persistent and transitory. This model extends `ConsGenIndShockModel` by allowing (log) persistent income to follow an AR(1) process.
#
# The agent's problem can be written in Bellman form as:
#
pGrid = np.linspace(0.1, 3.0, 24)
M = np.linspace(0.001, 5, 300)
for p in pGrid:
M_temp = M + ExplicitExample.solution[0].mLvlMin(p)
C = ExplicitExample.solution[0].vFunc(M_temp, p * np.ones_like(M_temp))
plt.plot(M_temp, C)
plt.ylim([-200, 0])
plt.xlabel("Market resource level mLvl")
plt.ylabel("Value v")
plt.show()
# %%
# Simulate some data
if do_simulation:
ExplicitExample.T_sim = 500
ExplicitExample.track_vars = ["mLvlNow", "cLvlNow", "pLvlNow"]
ExplicitExample.makeShockHistory() # This is optional
ExplicitExample.initializeSim()
ExplicitExample.simulate()
plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1))
plt.xlabel("Simulated time period")
plt.ylabel("Average market resources mLvl")
plt.show()
# %%
# Make and solve an example "persistent idisyncratic shocks" consumer
PersistentExample = PersistentShockConsumerType()
t_start = process_time()
PersistentExample.solve()
t_end = process_time()
print(
