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)
plt.ylabel('Consumption level cLvl')
plt.show()
# %%
# Plot the value function at various persistent income levels
if PersistentExample.vFuncBool:
pGrid = PersistentExample.pLvlGrid[0]
M = np.linspace(0.001, 5, 300)
for p in pGrid:
M_temp = M + PersistentExample.solution[0].mLvlMin(p)
C = PersistentExample.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
PersistentExample.T_sim = 500
PersistentExample.track_vars = ['mLvl', 'cLvl', 'pLvl']
PersistentExample.initialize_sim()
PersistentExample.simulate()
plt.plot(np.mean(PersistentExample.history['mLvl'], axis=1))
plt.xlabel('Simulated time period')
plt.ylabel('Average market resources mLvl')
plt.show()
# %%