class testKinkyPrefConsumerType(unittest.TestCase):
def setUp(self):
self.agent = KinkyPrefConsumerType()
self.agent.cycles = 0 # Infinite horizon
self.agent.solve()
def test_solution(self):
self.assertAlmostEqual(self.agent.solution[0].mNrmMin,
-0.7555156106287383)
m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)
self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[5],
0.6904681186891202)
c = self.agent.solution[0].cFunc(m, np.ones_like(m))
self.assertAlmostEqual(c[5], 0.13237946)
k = self.agent.solution[0].cFunc.derivativeX(m, np.ones_like(m))
self.assertAlmostEqual(k[5], 0.91443463)
self.agent.solution[0].vFunc
self.agent.solution[0].mNrmMin
def test_simulation(self):
self.agent.T_sim = 10
self.agent.track_vars = ["cNrmNow", "PrefShkNow"]
self.agent.initializeSim()
self.agent.simulate()
def setUp(self):
self.agent = KinkyPrefConsumerType()
self.agent.cycles = 0 # Infinite horizon
self.agent.solve()
#
# An example notebook which has some basic content which a REMARK may include.
#
# The REMARK notebook shouldn't just contain the code, use the markdown cells to explain the code and models.
# In[2]:
# required imports
from HARK.ConsumptionSaving.ConsPrefShockModel import KinkyPrefConsumerType
import numpy as np
import matplotlib.pyplot as plt
# In[3]:
# Make and solve a preference shock consumer
PrefShockExample = KinkyPrefConsumerType()
PrefShockExample.cycles = 0 # Infinite horizon
PrefShockExample.solve()
# In[4]:
# Unpack the consumption function for easier access.
PrefShockExample.unpack('cFunc')
PrefShockExample.unpack('mNrmMin')
# In[5]:
# Plot the consumption function at each discrete shock
m = np.linspace(PrefShockExample.mNrmMin[0], 5, 200)
for j in range(PrefShockExample.PrefShkDstn[0].pmf.size):
PrefShk = PrefShockExample.PrefShkDstn[0].X[j]
plotFuncs(
PrefShockExample.solution[0].vFunc,
PrefShockExample.solution[0].mNrmMin + 0.5,
5,
)
# Test the simulator for the pref shock class
if do_simulation:
PrefShockExample.T_sim = 120
PrefShockExample.track_vars = ["cNrmNow"]
PrefShockExample.makeShockHistory() # This is optional
PrefShockExample.initializeSim()
PrefShockExample.simulate()
# Make and solve a "kinky preferece" consumer, whose model combines KinkedR and PrefShock
KinkyPrefExample = KinkyPrefConsumerType()
KinkyPrefExample.cycles = 0 # Infinite horizon
t_start = process_time()
KinkyPrefExample.solve()
t_end = process_time()
print("Solving a kinky preference consumer took " + str(t_end - t_start) +
" seconds.")
# Plot the consumption function at each discrete shock
m = np.linspace(KinkyPrefExample.solution[0].mNrmMin, 5, 200)
print("Consumption functions at each discrete shock:")
for j in range(KinkyPrefExample.PrefShkDstn[0][1].size):
PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j]
c = KinkyPrefExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))
plt.plot(m, c)