"MrkvArray": [MrkvArray], # State transition probabilities
}
MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type
employed_income_dist = DiscreteDistribution(np.ones(1),
[np.ones(1), np.ones(1)]
) # Income distribution when employed
unemployed_income_dist = DiscreteDistribution(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 = process_time()
MarkovType.solve()
t_end = process_time()
MarkovType.unpack('cFunc')
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 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 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.unpack('cFunc')
self.TBSType = TBSType
self.MarkovType = MarkovType