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":[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
}
MarkovType = MarkovConsumerType(**Markov_primitives)
MarkovType.cycles = 0
employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)]
unemployed_income_dist = [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):
# 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],
"exp_nest":
3,
"LivPrb": [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
}
MarkovType = MarkovConsumerType(**Markov_primitives)
MarkovType.cycles = 0
employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)]
unemployed_income_dist = [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
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 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:
InfiniteType.tolerance = 0.0001
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 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:
short_type_list = [InfiniteType]
