def main_test(self):
Markov_vFuncBool_example = MarkovConsumerType(**Markov_Dict)
TranShkDstn_e = MeanOneLogNormal(
Markov_vFuncBool_example.TranShkStd[0],
123).approx(Markov_vFuncBool_example.TranShkCount)
TranShkDstn_u = DiscreteDistribution(np.ones(1), np.ones(1) * .2)
PermShkDstn = MeanOneLogNormal(
Markov_vFuncBool_example.PermShkStd[0],
123).approx(Markov_vFuncBool_example.PermShkCount)
#employed Income shock distribution
employed_IncShkDstn = combine_indep_dstns(PermShkDstn, TranShkDstn_e)
#unemployed Income shock distribution
unemployed_IncShkDstn = combine_indep_dstns(PermShkDstn, TranShkDstn_u)
# Specify list of IncShkDstns for each state
Markov_vFuncBool_example.IncShkDstn = [[
employed_IncShkDstn, unemployed_IncShkDstn
]]
#solve the consumer's problem
Markov_vFuncBool_example.solve()
self.assertAlmostEqual(
Markov_vFuncBool_example.solution[0].vFunc[1](0.4),
-4.127935542867632)
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 initializeSim(self):
MarkovConsumerType.initializeSim(self)
if hasattr(self,'T_advance'):
self.restoreState()
self.MrkvArray = self.MrkvArray_sim
elif not hasattr(self,'mortality_off'):
self.calcAgeDistribution()
self.initializeAges()
if (hasattr(self,'Mrkv_univ') and self.Mrkv_univ is not None):
self.MrkvNow[:] = self.Mrkv_univ
def getMarkovStates(self):
'''
A modified method that forces all agents to be in a particular Markov
state when the attribute Mrkv_univ is not None. This allows us to draw
income shocks for every Markov state for each agent in each simulated
period when pre-specifying the shocks. When the model is *actually*
simulated, this ensures that agent i in period t and Markov state k will
get the same income shocks *no matter which specification we use*.
'''
MarkovConsumerType.getMarkovStates(self) # Basic Markov state draw
if (hasattr(self, 'Mrkv_univ') and self.Mrkv_univ is not None):
self.MrkvNow_temp = self.MrkvNow
self.MrkvNow = self.Mrkv_univ * np.ones(self.AgentCount, dtype=int)
def test_MarkovConsumerType(self):
try:
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)]])
# Make a consumer with serially correlated unemployment, subject to boom and bust cycles
init_serial_unemployment = copy(init_idiosyncratic_shocks)
init_serial_unemployment['MrkvArray'] = [MrkvArray]
init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed
init_serial_unemployment['global_markov'] = False
SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)
except:
self.fail("MarkovConsumerType failed to initialize with boom/bust unemployment.")
def getStates(self):
MarkovConsumerType.getStates(self)
if hasattr(self,'T_advance'): # This means we're in the policy experiment
self.noticeStimulus()
self.makeWeights()
if hasattr(self,'ContUnempBenefits'):
if self.ContUnempBenefits==True:
self.continueUnemploymentBenefits()
# Store indicators of whether this agent is a worker and unemployed
w = self.t_cycle <= self.T_retire
u = np.logical_and(np.mod(self.MrkvNow,3) > 0, w)
lLvl = self.pLvlNow*self.TranShkNow
lLvl[u] = 0.
lLvl[self.t_cycle > self.T_retire] = 0.
self.lLvlNow = lLvl
self.uNow = u
self.wNow = w
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
self.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(Params.init_idiosyncratic_shocks)
init_serial_unemployment["MrkvArray"] = [self.MrkvArray]
self.model = MarkovConsumerType(**init_serial_unemployment)
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()
def simDeath(self):
if hasattr(self,'mortality_off'):
return np.zeros(self.AgentCount, dtype=bool)
else:
return MarkovConsumerType.simDeath(self)
# %% [markdown]
# One other parameter needs to change: the number of agents in simulation. We want to increase this, because later on when we vastly increase the variance of the permanent income shock, things get wonky. (We need to change this value here, before we have used the parameters to initialize the $\texttt{MarkovConsumerType}$, because this parameter is used during initialization.)
#
# Other parameters that are not used during initialization can also be assigned here, by changing the appropriate value in the $\texttt{init_China_parameters_dictionary}$; however, they can also be changed later, by altering the appropriate attribute of the initialized $\texttt{MarkovConsumerType}$.
# %%
init_China_parameters['AgentCount'] = 10000
# %% [markdown]
# ### Import and initialize the Agents
#
# Here, we bring in an agent making a consumption/savings decision every period, subject to transitory and permanent income shocks, AND a Markov shock
# %%
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# %% [markdown]
# Currently, Markov states can differ in their interest factor, permanent growth factor, survival probability, and income distribution. Each of these needs to be specifically set.
#
# Do that here, except shock distribution, which will be done later (because we want to examine the consequences of different shock distributions).
# %%
GrowthFastAnn = 1.06 # Six percent annual growth
GrowthSlowAnn = 1.00 # Stagnation
ChinaExample.assignParameters(PermGroFac = [np.array([GrowthSlow.,GrowthFast ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #needs to be an array, of shape (StateCount,)
LivPrb = [np.array(StateCount*[init_China_parameters['LivPrb']][0])], #needs to be a list, with 0th element of shape of shape (StateCount,)
cycles = 0)
ChinaExample.track_vars = ['aNrmNow','cNrmNow','pLvlNow'] # Names of variables to be tracked
def preSolve(self):
self.MrkvArray = self.MrkvArray_pcvd
MarkovConsumerType.preSolve(self)
self.updateSolutionTerminal()
def reset_rng(self):
MarkovConsumerType.reset_rng(self)
def setUp(self):
# Set up and solve TBS
base_primitives = {'UnempPrb': .015,
'DiscFac': 0.9,
'Rfree': 1.1,
'PermGroFac': 1.05,
'CRRA': .95}
TBSType = TractableConsumerType(**base_primitives)
TBSType.solve()
# Set up and solve Markov
MrkvArray = [np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']],[0.0, 1.0]])]
Markov_primitives = {"CRRA": base_primitives['CRRA'],
"Rfree": np.array(2*[base_primitives['Rfree']]),
"PermGroFac": [np.array(2*[base_primitives['PermGroFac'] /
(1.0-base_primitives['UnempPrb'])])],
"BoroCnstArt": None,
"PermShkStd": [0.0],
"PermShkCount": 1,
"TranShkStd": [0.0],
"TranShkCount": 1,
"T_total": 1,
"UnempPrb": 0.0,
"UnempPrbRet": 0.0,
"T_retire": 0,
"IncUnemp": 0.0,
"IncUnempRet": 0.0,
"aXtraMin": 0.001,
"aXtraMax": TBSType.mUpperBnd,
"aXtraCount": 48,
"aXtraExtra": [None],
"aXtraNestFac": 3,
"LivPrb":[np.array([1.0,1.0]),],
"DiscFac": base_primitives['DiscFac'],
'Nagents': 1,
'psi_seed': 0,
'xi_seed': 0,
'unemp_seed': 0,
'tax_rate': 0.0,
'vFuncBool': False,
'CubicBool': True,
'MrkvArray': MrkvArray,
'T_cycle':1
}
MarkovType = MarkovConsumerType(**Markov_primitives)
MarkovType.cycles = 0
employed_income_dist = DiscreteDistribution(np.ones(1),
[np.ones(1),
np.ones(1)])
unemployed_income_dist = DiscreteDistribution(np.ones(1),
[np.ones(1),
np.zeros(1)])
MarkovType.IncomeDstn = [[employed_income_dist,
unemployed_income_dist]]
MarkovType.solve()
MarkovType.unpackcFunc()
self.TBSType = TBSType
self.MarkovType = MarkovType
def __init__(self,cycles=1,time_flow=True,**kwds):
MarkovConsumerType.__init__(self,cycles=1,time_flow=True,**kwds)
self.solveOnePeriod = solveConsMarkovALT
def main():
# Import the HARK library. The assumption is that this code is in a folder
# contained in the HARK folder. Also import the ConsumptionSavingModel
import numpy as np # numeric Python
from HARK.utilities import plotFuncs # basic plotting tools
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType # An alternative, much longer way to solve the TBS model
from time import clock # timing utility
do_simulation = True
# Define the model primitives
base_primitives = {
'UnempPrb': .00625, # Probability of becoming unemployed
'DiscFac': 0.975, # Intertemporal discount factor
'Rfree': 1.01, # Risk-free interest factor on assets
'PermGroFac': 1.0025, # Permanent income growth factor (uncompensated)
'CRRA': 1.0
} # Coefficient of relative risk aversion
# Define a dictionary to be used in case of simulation
simulation_values = {
'aLvlInitMean': 0.0, # Mean of log initial assets for new agents
'aLvlInitStd': 1.0, # Stdev of log initial assets for new agents
'AgentCount': 10000, # Number of agents to simulate
'T_sim': 120, # Number of periods to simulate
'T_cycle': 1
} # Number of periods in the cycle
# Make and solve a tractable consumer type
ExampleType = TractableConsumerType(**base_primitives)
t_start = clock()
ExampleType.solve()
t_end = clock()
print('Solving a tractable consumption-savings model took ' +
str(t_end - t_start) + ' seconds.')
# Plot the consumption function and whatnot
m_upper = 1.5 * ExampleType.mTarg
conFunc_PF = lambda m: ExampleType.h * ExampleType.PFMPC + ExampleType.PFMPC * m
#plotFuncs([ExampleType.solution[0].cFunc,ExampleType.mSSfunc,ExampleType.cSSfunc],0,m_upper)
plotFuncs([ExampleType.solution[0].cFunc, ExampleType.solution[0].cFunc_U],
0, m_upper)
if do_simulation:
ExampleType(**
simulation_values) # Set attributes needed for simulation
ExampleType.track_vars = ['mLvlNow']
ExampleType.makeShockHistory()
ExampleType.initializeSim()
ExampleType.simulate()
# Now solve the same model using backward induction rather than the analytic method of TBS.
# The TBS model is equivalent to a Markov model with two states, one of them absorbing (permanent unemployment).
MrkvArray = np.array(
[[1.0 - base_primitives['UnempPrb'], base_primitives['UnempPrb']],
[0.0,
1.0]]) # Define the two state, absorbing unemployment Markov array
init_consumer_objects = {
"CRRA":
base_primitives['CRRA'],
"Rfree":
np.array(2 * [base_primitives['Rfree']
]), # Interest factor (same in both states)
"PermGroFac": [
np.array(2 * [
base_primitives['PermGroFac'] /
(1.0 - base_primitives['UnempPrb'])
])
], # Unemployment-compensated permanent growth factor
"BoroCnstArt":
None, # Artificial borrowing constraint
"PermShkStd": [0.0], # Permanent shock standard deviation
"PermShkCount":
1, # Number of shocks in discrete permanent shock distribution
"TranShkStd": [0.0], # Transitory shock standard deviation
"TranShkCount":
1, # Number of shocks in discrete permanent shock distribution
"T_cycle":
1, # Number of periods in cycle
"UnempPrb":
0.0, # Unemployment probability (not used, as the unemployment here is *permanent*, not transitory)
"UnempPrbRet":
0.0, # Unemployment probability when retired (irrelevant here)
"T_retire":
0, # Age at retirement (turned off)
"IncUnemp":
0.0, # Income when unemployed (irrelevant)
"IncUnempRet":
0.0, # Income when unemployed and retired (irrelevant)
"aXtraMin":
0.001, # Minimum value of assets above minimum in grid
"aXtraMax":
ExampleType.mUpperBnd, # Maximum value of assets above minimum in grid
"aXtraCount":
48, # Number of points in assets grid
"aXtraExtra": [None], # Additional points to include in assets grid
"aXtraNestFac":
3, # Degree of exponential nesting when constructing assets grid
"LivPrb": [np.array([1.0, 1.0])], # Survival probability
"DiscFac":
base_primitives['DiscFac'], # Intertemporal discount factor
'AgentCount':
1, # Number of agents in a simulation (irrelevant)
'tax_rate':
0.0, # Tax rate on labor income (irrelevant)
'vFuncBool':
False, # Whether to calculate the value function
'CubicBool':
True, # Whether to use cubic splines (False --> linear splines)
'MrkvArray': [MrkvArray] # State transition probabilities
}
MarkovType = MarkovConsumerType(
**init_consumer_objects) # Make a basic consumer type
employed_income_dist = [np.ones(1), np.ones(1),
np.ones(1)] # Income distribution when employed
unemployed_income_dist = [
np.ones(1), np.ones(1), np.zeros(1)
] # Income distribution when permanently unemployed
MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]
] # set the income distribution in each state
MarkovType.cycles = 0
# Solve the "Markov TBS" model
t_start = clock()
MarkovType.solve()
t_end = clock()
MarkovType.unpackcFunc()
print('Solving the same model "the long way" took ' +
str(t_end - t_start) + ' seconds.')
#plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
plotFuncs(MarkovType.cFunc[0], 0, m_upper)
diffFunc = lambda m: ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[
0][0](m)
print('Difference between the (employed) consumption functions:')
plotFuncs(diffFunc, 0, m_upper)
def resetRNG(self):
MarkovConsumerType.resetRNG(self)
# %% [markdown]
# One other parameter needs to change: the number of agents in simulation. We want to increase this, because later on when we vastly increase the variance of the permanent income shock, things get wonky. (We need to change this value here, before we have used the parameters to initialize the $\texttt{MarkovConsumerType}$, because this parameter is used during initialization.)
#
# Other parameters that are not used during initialization can also be assigned here, by changing the appropriate value in the $\texttt{init_China_parameters_dictionary}$; however, they can also be changed later, by altering the appropriate attribute of the initialized $\texttt{MarkovConsumerType}$.
# %%
init_China_parameters['AgentCount'] = 10000
# %% [markdown]
# ### Import and initialize the Agents
#
# Here, we bring in an agent making a consumption/savings decision every period, subject to transitory and permanent income shocks, AND a Markov shock
# %%
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# %% [markdown]
# Currently, Markov states can differ in their interest factor, permanent growth factor, survival probability, and income distribution. Each of these needs to be specifically set.
#
# Do that here, except shock distribution, which will be done later (because we want to examine the consequences of different shock distributions).
# %%
GrowthFastAnn = 1.06 # Six percent annual growth
GrowthSlowAnn = 1.00 # Stagnation
ChinaExample.assignParameters(PermGroFac = [np.array([GrowthSlowAnn,GrowthFastAnn ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #needs to be an array, of shape (StateCount,)
LivPrb = [np.array(StateCount*[init_China_parameters['LivPrb']][0])], #needs to be a list, with 0th element of shape of shape (StateCount,)
cycles = 0)
ChinaExample.track_vars = ['aNrmNow','cNrmNow','pLvlNow'] # Names of variables to be tracked
# 1. Model with serially correlated unemployment
# 2. Model with period of "unemployment immunity"
# 3. Model with serially correlated permanent income growth
# 4. Model with serially correlated interest factor
#
# ### 1. Serial Unemployment
#
# Let's create a consumer similar to the one in "idiosyncratic shock" model but who faces serially correlated unemployment during boom or bust cycles of the economy.
# %%
# Make a consumer with serially correlated unemployment, subject to boom and bust cycles
init_serial_unemployment = copy(init_idiosyncratic_shocks)
init_serial_unemployment["MrkvArray"] = [MrkvArray]
init_serial_unemployment["UnempPrb"] = 0 # to make income distribution when employed
init_serial_unemployment["global_markov"] = False
SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)
SerialUnemploymentExample.cycles = 0
SerialUnemploymentExample.vFuncBool = False # for easy toggling here
# %%
# Replace the default (lognormal) income distribution with a custom one
employed_income_dist = DiscreteDistribution(np.ones(1), [np.ones(1), np.ones(1)]) # Definitely get income
unemployed_income_dist = DiscreteDistribution(np.ones(1), [np.ones(1), np.zeros(1)]) # Definitely don't
SerialUnemploymentExample.IncomeDstn = [
[
employed_income_dist,
unemployed_income_dist,
employed_income_dist,
unemployed_income_dist,
]
]
"aXtraNestFac":
3, # Degree of exponential nesting when constructing assets grid
"LivPrb": [np.array([1.0, 1.0])], # Survival probability
"DiscFac":
base_primitives["DiscFac"], # Intertemporal discount factor
"AgentCount":
1, # Number of agents in a simulation (irrelevant)
"tax_rate":
0.0, # Tax rate on labor income (irrelevant)
"vFuncBool":
False, # Whether to calculate the value function
"CubicBool":
True, # Whether to use cubic splines (False --> linear splines)
"MrkvArray": [MrkvArray], # State transition probabilities
}
MarkovType = MarkovConsumerType(
**init_consumer_objects) # Make a basic consumer type
employed_income_dist = [
np.ones(1),
np.ones(1),
np.ones(1),
] # Income distribution when employed
unemployed_income_dist = [
np.ones(1),
np.ones(1),
np.zeros(1),
] # Income distribution when permanently unemployed
MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]
] # set the income distribution in each state
MarkovType.cycles = 0
# %%
def setUp(self):
# Set up and solve TBS
base_primitives = {
"UnempPrb": 0.015,
"DiscFac": 0.9,
"Rfree": 1.1,
"PermGroFac": 1.05,
"CRRA": 0.95,
}
TBSType = TractableConsumerType(**base_primitives)
TBSType.solve()
# Set up and solve Markov
MrkvArray = [
np.array([
[
1.0 - base_primitives["UnempPrb"],
base_primitives["UnempPrb"]
],
[0.0, 1.0],
])
]
Markov_primitives = {
"CRRA":
base_primitives["CRRA"],
"Rfree":
np.array(2 * [base_primitives["Rfree"]]),
"PermGroFac": [
np.array(2 * [
base_primitives["PermGroFac"] /
(1.0 - base_primitives["UnempPrb"])
])
],
"BoroCnstArt":
None,
"PermShkStd": [0.0],
"PermShkCount":
1,
"TranShkStd": [0.0],
"TranShkCount":
1,
"T_total":
1,
"UnempPrb":
0.0,
"UnempPrbRet":
0.0,
"T_retire":
0,
"IncUnemp":
0.0,
"IncUnempRet":
0.0,
"aXtraMin":
0.001,
"aXtraMax":
TBSType.mUpperBnd,
"aXtraCount":
48,
"aXtraExtra": [None],
"aXtraNestFac":
3,
"LivPrb": [
np.array([1.0, 1.0]),
],
"DiscFac":
base_primitives["DiscFac"],
"Nagents":
1,
"psi_seed":
0,
"xi_seed":
0,
"unemp_seed":
0,
"tax_rate":
0.0,
"vFuncBool":
False,
"CubicBool":
True,
"MrkvArray":
MrkvArray,
"T_cycle":
1,
}
MarkovType = MarkovConsumerType(**Markov_primitives)
MarkovType.cycles = 0
employed_income_dist = DiscreteDistribution(np.ones(1),
np.array([[1.0], [1.0]]))
unemployed_income_dist = DiscreteDistribution(np.ones(1),
np.array([[1.0], [0.0]]))
MarkovType.IncShkDstn = [[
employed_income_dist, unemployed_income_dist
]]
MarkovType.solve()
MarkovType.unpack("cFunc")
self.TBSType = TBSType
self.MarkovType = MarkovType
def getShocks(self):
MarkovConsumerType.getShocks(self)
if (hasattr(self, 'Mrkv_univ') and self.Mrkv_univ is not None):
self.MrkvNow = self.MrkvNow_temp # Make sure real sequence is recorded
