class testTractableBufferStock(unittest.TestCase):
def setUp(self):
# Create portfolio choice consumer type
self.tct = TractableConsumerType()
# Solve the model under the given parameters
self.tct.solve()
def test_simulation(self):
self.tct.T_sim = 30 # Number of periods to simulate
self.tct.AgentCount = 10 # Number of agents to simulate
self.tct.aLvlInitMean = 0.0 # Mean of log initial assets for new agents
self.tct.aLvlInitStd = 1.0 # stdev of log initial assets for new agents
self.tct.T_cycle = 1
self.tct.track_vars += ["mLvl"]
self.tct.initialize_sim()
self.tct.simulate()
self.assertAlmostEqual(self.tct.history["mLvl"][15][0], 5.820630251772332)
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
# \begin{align}
# \check{m} & = 1 + \left(\frac{1}{(\gamma-r)+(1+(\gamma/\mho)(1-(\gamma/\mho)(\rho-1)/2))}\right)
# \end{align}
#
# %% {"code_folding": [0]}
# Define a parameter dictionary and representation of the agents for the tractable buffer stock model
TBS_dictionary = {
'UnempPrb':
.00625, # Prob of becoming unemployed; working life of 1/UnempProb = 160 qtrs
'DiscFac': 0.975, # Intertemporal discount factor
'Rfree': 1.01, # Risk-free interest factor on assets
'PermGroFac': 1.0025, # Permanent income growth factor (uncompensated)
'CRRA': 2.5
} # Coefficient of relative risk aversion
MyTBStype = TractableConsumerType(**TBS_dictionary)
# %% [markdown]
# ## Target Wealth
#
# Whether the model exhibits a "target" or "stable" level of the wealth-to-permanent-income ratio for employed consumers depends on whether the 'Growth Impatience Condition' (the GIC) holds:
#
# \begin{align}\label{eq:GIC}
# \left(\frac{(R \beta (1-\mho))^{1/\rho}}{\Gamma}\right) & < 1
# \\ \left(\frac{(R \beta (1-\mho))^{1/\rho}}{G (1-\mho)}\right) &< 1
# \\ \left(\frac{(R \beta)^{1/\rho}}{G} (1-\mho)^{-\rho}\right) &< 1
# \end{align}
# and recall (from [PerfForesightCRRA](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/)) that the perfect foresight 'Growth Impatience Factor' is
# \begin{align}\label{eq:PFGIC}
# \left(\frac{(R \beta)^{1/\rho}}{G}\right) &< 1
# \end{align}
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 setUp(self):
# Create portfolio choice consumer type
self.tct = TractableConsumerType()
# Solve the model under the given parameters
self.tct.solve()
"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 = process_time()
ExampleType.solve()
t_end = process_time()
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)
# %%
InfiniteType.a_init = 0 * np.ones_like(a_init)
# Make histories of permanent income levels for the infinite horizon type
p_init_base = np.ones(Params.sim_pop_size, dtype=float)
InfiniteType.p_init = p_init_base
# Use a "tractable consumer" instead if desired.
# If you want this to work, you must edit TractableBufferStockModel slightly.
# See comments around line 34 in that module for instructions.
if Params.do_tractable:
from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType
TractableInfType = TractableConsumerType(
DiscFac=0.99, # will be overwritten
UnempPrb=1 - InfiniteType.LivPrb[0],
Rfree=InfiniteType.Rfree,
PermGroFac=InfiniteType.PermGroFac[0],
CRRA=InfiniteType.CRRA,
sim_periods=InfiniteType.sim_periods,
IncUnemp=InfiniteType.IncUnemp,
Nagents=InfiniteType.Nagents)
TractableInfType.p_init = InfiniteType.p_init
TractableInfType.timeFwd()
TractableInfType.TranShkHist = InfiniteType.TranShkHist
TractableInfType.PermShkHist = InfiniteType.PermShkHist
TractableInfType.a_init = InfiniteType.a_init
# Set the type list for the infinite horizon estimation
if Params.do_tractable:
short_type_list = [TractableInfType]
spec_add = 'TC'
else:
R, mE): # Function that gives us locus of where change in mE is zero
cE = (1 - R**(-1)) * mE + R**(-1)
return cE
def cEDelEqZero(
R, k, Pi,
mE): # Function that gives us locus of where change in cE is zero
cE = (R * k * mE * Pi) / (1 + R * k * Pi)
return cE
# %% {"code_folding": []}
# Define and solve the model
MyTBStype = TractableConsumerType(**TBS_dictionary)
MyTBStype.solve()
UnempPrb = MyTBStype.UnempPrb
DiscFac = MyTBStype.DiscFac
R = MyTBStype.Rfree
PermGroFacCmp = MyTBStype.PermGroFacCmp
k = MyTBStype.PFMPC
CRRA = MyTBStype.CRRA
Rnrm = MyTBStype.Rnrm
Pi = PiFunc(R, DiscFac, CRRA, UnempPrb, PermGroFacCmp)
# Define the range of mE to plot over
mE_range = np.linspace(0, 8, 1000)
cE = MyTBStype.solution[0].cFunc(mE_range)