class test_ConsMarkovSolver(unittest.TestCase):
def setUp(self):
# Define the Markov transition matrix for serially correlated unemployment
unemp_length = 5 # Averange length of unemployment spell
urate_good = 0.05 # Unemployment rate when economy is in good state
urate_bad = 0.12 # Unemployment rate when economy is in bad state
bust_prob = 0.01 # Probability of economy switching from good to bad
recession_length = 20 # Averange length of bad state
p_reemploy = 1.0 / unemp_length
p_unemploy_good = p_reemploy * urate_good / (1 - urate_good)
p_unemploy_bad = p_reemploy * urate_bad / (1 - urate_bad)
boom_prob = 1.0 / recession_length
MrkvArray = np.array([
[
(1 - p_unemploy_good) * (1 - bust_prob),
p_unemploy_good * (1 - bust_prob),
(1 - p_unemploy_good) * bust_prob,
p_unemploy_good * bust_prob,
],
[
p_reemploy * (1 - bust_prob),
(1 - p_reemploy) * (1 - bust_prob),
p_reemploy * bust_prob,
(1 - p_reemploy) * bust_prob,
],
[
(1 - p_unemploy_bad) * boom_prob,
p_unemploy_bad * boom_prob,
(1 - p_unemploy_bad) * (1 - boom_prob),
p_unemploy_bad * (1 - boom_prob),
],
[
p_reemploy * boom_prob,
(1 - p_reemploy) * boom_prob,
p_reemploy * (1 - boom_prob),
(1 - p_reemploy) * (1 - boom_prob),
],
])
init_serial_unemployment = copy(init_idiosyncratic_shocks)
init_serial_unemployment["MrkvArray"] = [MrkvArray]
init_serial_unemployment[
"UnempPrb"] = 0.0 # to make income distribution when employed
init_serial_unemployment["global_markov"] = False
self.model = MarkovConsumerType(**init_serial_unemployment)
self.model.cycles = 0
self.model.vFuncBool = False # for easy toggling here
# Replace the default (lognormal) income distribution with a custom one
employed_income_dist = DiscreteDistribution(
np.ones(1), np.array([[1.0], [1.0]])) # Definitely get income
unemployed_income_dist = DiscreteDistribution(
np.ones(1), np.array([[1.0], [0.0]])) # Definitely don't
self.model.IncShkDstn = [[
employed_income_dist,
unemployed_income_dist,
employed_income_dist,
unemployed_income_dist,
]]
def test_check_markov_inputs(self):
# check Rfree
self.assertRaises(ValueError, self.model.check_markov_inputs)
# fix Rfree
self.model.Rfree = np.array(4 * [self.model.Rfree])
# check MrkvArray, first mess up the setup
self.MrkvArray = self.model.MrkvArray
self.model.MrkvArray = np.random.rand(3, 3)
self.assertRaises(ValueError, self.model.check_markov_inputs)
# then fix it back
self.model.MrkvArray = self.MrkvArray
# check LivPrb
self.assertRaises(ValueError, self.model.check_markov_inputs)
# fix LivPrb
self.model.LivPrb = [np.array(4 * self.model.LivPrb)]
# check PermGroFac
self.assertRaises(ValueError, self.model.check_markov_inputs)
# fix PermGroFac
self.model.PermGroFac = [np.array(4 * self.model.PermGroFac)]
def test_solve(self):
self.model.Rfree = np.array(4 * [self.model.Rfree])
self.model.LivPrb = [np.array(4 * self.model.LivPrb)]
self.model.PermGroFac = [np.array(4 * self.model.PermGroFac)]
self.model.solve()
def test_simulation(self):
self.model.Rfree = np.array(4 * [self.model.Rfree])
self.model.LivPrb = [np.array(4 * self.model.LivPrb)]
self.model.PermGroFac = [np.array(4 * self.model.PermGroFac)]
self.model.solve()
self.model.T_sim = 120
self.model.MrkvPrbsInit = [0.25, 0.25, 0.25, 0.25]
self.model.track_vars = ["mNrm", 'cNrm']
self.model.make_shock_history() # This is optional
self.model.initialize_sim()
self.model.simulate()
)
print("Consumption functions for each discrete state:")
plotFuncs(SerialUnemploymentExample.solution[0].cFunc, 0, 50)
if SerialUnemploymentExample.vFuncBool:
print("Value functions for each discrete state:")
plotFuncs(SerialUnemploymentExample.solution[0].vFunc, 5, 50)
# %%
# Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist
if do_simulation:
SerialUnemploymentExample.T_sim = 120
SerialUnemploymentExample.MrkvPrbsInit = [0.25, 0.25, 0.25, 0.25]
SerialUnemploymentExample.track_vars = ["mNrmNow", "cNrmNow"]
SerialUnemploymentExample.makeShockHistory() # This is optional
SerialUnemploymentExample.initializeSim()
SerialUnemploymentExample.simulate()
# %% [markdown]
# ### 2. Unemployment immunity for a fixed period
#
# Let's create a consumer similar to the one in "idiosyncratic shock" model but who occasionally gets "unemployment immunity" for a fixed period in an economy subject to boom and bust cycles.
# %%
# Make a consumer who occasionally gets "unemployment immunity" for a fixed period
UnempPrb = 0.05 # Probability of becoming unemployed each period
ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment
ImmunityT = 6 # Number of periods of immunity
# %%
StateCount = ImmunityT + 1 # Total number of Markov states
IncomeDstnReg = DiscreteDistribution(
