def setUp(self):
# "persistent idiosyncratic shocks" model
PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income
persistent_shocks = copy(GenIncDictionary)
persistent_shocks["PrstIncCorr"] = PrstIncCorr
# "persistent idisyncratic shocks" consumer
self.agent = PersistentShockConsumerType(cycles=1, **persistent_shocks)
self.agent.solve()
class testPersistentShockConsumerType(unittest.TestCase):
def setUp(self):
# "persistent idiosyncratic shocks" model
PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income
persistent_shocks = copy(GenIncDictionary)
persistent_shocks["PrstIncCorr"] = PrstIncCorr
# "persistent idisyncratic shocks" consumer
self.agent = PersistentShockConsumerType(cycles=1, **persistent_shocks)
self.agent.solve()
def test_solution(self):
pLvlGrid = self.agent.pLvlGrid[0]
self.assertAlmostEqual(
self.agent.solution[0].cFunc(10, pLvlGrid[1]).tolist(),
5.6030075768585075)
def test_simulation(self):
self.agent.T_sim = 25
# why does ,"bLvlNow" not work here?
self.agent.track_vars = ["aLvl", "mLvl", "cLvl", "pLvl"]
self.agent.initialize_sim()
self.agent.simulate()
self.assertAlmostEqual(np.mean(self.agent.history["mLvl"]),
1.2043946738813716)
class testPersistentShockConsumerType(unittest.TestCase):
def setUp(self):
# "persistent idiosyncratic shocks" model
PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income
persistent_shocks = copy(GenIncDictionary)
persistent_shocks['PrstIncCorr'] = PrstIncCorr
# "persistent idisyncratic shocks" consumer
self.agent = PersistentShockConsumerType(cycles=1, **persistent_shocks)
self.agent.solve()
def test_solution(self):
pLvlGrid = self.agent.pLvlGrid[0]
self.assertAlmostEqual(
self.agent.solution[0].cFunc(10, pLvlGrid[1]).tolist(),
5.6030075768585075)
def test_simulation(self):
self.agent.T_sim = 25
self.agent.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow']
self.agent.initializeSim()
self.agent.simulate()
self.assertAlmostEqual(np.mean(self.agent.history['mLvlNow']),
1.2043701902476343)
class testPersistentShockConsumerType(unittest.TestCase):
def setUp(self):
# "persistent idiosyncratic shocks" model
PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income
persistent_shocks = copy(GenIncDictionary)
persistent_shocks['PrstIncCorr'] = PrstIncCorr
# "persistent idisyncratic shocks" consumer
self.agent = PersistentShockConsumerType(**persistent_shocks)
self.agent.solve()
def test_solution(self):
pLvlGrid = self.agent.pLvlGrid[0]
self.assertAlmostEqual(
self.agent.solution[0].cFunc(10, pLvlGrid[1]).tolist(),
0.9141970598224893)
def test_simulation(self):
self.agent.T_sim = 25
self.agent.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow']
self.agent.initializeSim()
self.agent.simulate()
self.assertAlmostEqual(np.mean(self.agent.mLvlNow_hist),
2.372861738538392)
# * For this model, we overwrite the method $\texttt{updatepLvlNextFunc}$ to create the input $\texttt{pLvlNextFunc}$ as a sequence of AR1-style functions. The method uses now the attributes $\texttt{PermGroFac}$ and $\texttt{PrstIncCorr}$. If cycles=0, the product of $\texttt{PermGroFac}$ across all periods must be 1.0, otherwise this method is invalid.
#
# %% {"code_folding": []}
# Make a dictionary for the "persistent idiosyncratic shocks" model
PrstIncCorr = 0.98 # Serial correlation coefficient for persistent income
persistent_shocks = copy(GenIncDictionary)
persistent_shocks['PrstIncCorr'] = PrstIncCorr
# %% [markdown]
# The `PersistentShockConsumerType` class solves the problem of a consumer facing idiosyncratic shocks to his persistent and transitory income, and for which the (log) persistent income follows an AR1 process rather than random walk.
# %%
# Make and solve an example of "persistent idisyncratic shocks" consumer
PersistentExample = PersistentShockConsumerType(**persistent_shocks)
PersistentExample.solve()
# %%
# Plot the consumption function at various levels of persistent income pLvl
print(
'Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of '
+ str(PersistentExample.PrstIncCorr) + ':')
pLvlGrid = PersistentExample.pLvlGrid[0]
mLvlGrid = np.linspace(0, 20, 300)
for p in pLvlGrid:
M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p)
C = PersistentExample.solution[0].cFunc(M_temp, p * np.ones_like(M_temp))
plt.plot(M_temp, C)
plt.xlim(0., 20.)
plt.xlabel('Market resource level mLvl')
# %%
# 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(
"Solving a persistent income shocks consumer took "
+ mystr(t_end - t_start)
+ " seconds."
)
# %%
# Plot the consumption function at various levels of persistent income pLvl
print(
"Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of "
+ str(PersistentExample.PrstIncCorr)
+ ":"
