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):
# 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