class testPrefShockConsumerType(unittest.TestCase):
def setUp(self):
self.agent = PrefShockConsumerType()
self.agent.cycles = 0
self.agent.solve()
def test_solution(self):
self.assertEqual(self.agent.solution[0].mNrmMin, 0)
m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)
self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[5], 0.69046812)
self.assertAlmostEqual(
self.agent.solution[0].cFunc(m, np.ones_like(m))[35],
0.8123891603954809)
self.assertAlmostEqual(
self.agent.solution[0].cFunc.derivativeX(m, np.ones_like(m))[35],
0.44973706445183886)
def test_simulation(self):
self.agent.T_sim = 10
self.agent.track_vars = ["cNrmNow"]
self.agent.makeShockHistory() # This is optional
self.agent.initializeSim()
self.agent.simulate()
class testPrefShockConsumerType(unittest.TestCase):
def setUp(self):
self.agent = PrefShockConsumerType()
self.agent.cycles = 0
self.agent.solve()
def test_solution(self):
self.assertEqual(self.agent.solution[0].mNrmMin, 0)
m = np.linspace(self.agent.solution[0].mNrmMin, 5, 200)
self.assertAlmostEqual(self.agent.PrefShkDstn[0].X[0, 5], 0.69046812)
self.assertAlmostEqual(
self.agent.solution[0].cFunc(m, np.ones_like(m))[35],
0.8123891603954809)
self.assertAlmostEqual(
self.agent.solution[0].cFunc.derivativeX(m, np.ones_like(m))[35],
0.44973706445183886,
)
def test_simulation(self):
self.agent.T_sim = 10
self.agent.track_vars = ['cNrm', "PrefShk"]
self.agent.make_shock_history() # This is optional
self.agent.initialize_sim()
self.agent.simulate()
self.assertAlmostEqual(self.agent.history['cNrm'][0][5],
0.7366020536567589)
self.assertEqual(
self.agent.shock_history["PrefShk"][0][5],
self.agent.history["PrefShk"][0][5],
)
self.assertEqual(self.agent.history["PrefShk"][0][5],
0.4909415933881665)
def setUp(self):
self.agent = PrefShockConsumerType()
self.agent.cycles = 0
self.agent.solve()
import HARK.ConsumptionSaving.ConsumerParameters as Params
import matplotlib.pyplot as plt
from HARK.utilities import plotFuncs
from time import process_time
import numpy as np
from HARK.ConsumptionSaving.ConsPrefShockModel import (
PrefShockConsumerType,
KinkyPrefConsumerType,
)
mystr = lambda number: "{:.4f}".format(number)
do_simulation = True
# Make and solve a preference shock consumer
PrefShockExample = PrefShockConsumerType()
PrefShockExample.cycles = 0 # Infinite horizon
t_start = process_time()
PrefShockExample.solve()
t_end = process_time()
print("Solving a preference shock consumer took " + str(t_end - t_start) +
" seconds.")
# Plot the consumption function at each discrete shock
m = np.linspace(PrefShockExample.solution[0].mNrmMin, 5, 200)
print("Consumption functions at each discrete shock:")
for j in range(PrefShockExample.PrefShkDstn[0][1].size):
PrefShk = PrefShockExample.PrefShkDstn[0][1][j]
c = PrefShockExample.solution[0].cFunc(m, PrefShk * np.ones_like(m))
plt.plot(m, c)
plt.xlim([0.0, None])
plt.ylim([0.0, None])