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
# One other parameter 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.
# It is important to note that we need to change this value here, before we have used the parameters
# to initialize the MarkovConsumerType. This is 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 init_China_parameters_dictionary; however,
# they can also be changed later, by altering the appropriate attribute of the initialized
# MarkovConsumerType.
init_China_parameters['AgentCount'] = 10000
### Import and initialize the HARK ConsumerType we want
### 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 ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# 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 income distribution. That will be done later, because we want to examine
# the effects of different income distributions.
ChinaExample.assignParameters(
PermGroFac=[
np.array([1., 1.06**(.25)])
], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree=np.array(StateCount *
[init_China_parameters['Rfree']
]), #need to be an array, of shape (StateCount,)
LivPrb=[
np.array(StateCount * [init_China_parameters['LivPrb']][0])
"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)
# One other parameter 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.
# It is important to note that we need to change this value here, before we have used the parameters
# to initialize the MarkovConsumerType. This is 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 init_China_parameters_dictionary; however,
# they can also be changed later, by altering the appropriate attribute of the initialized
# MarkovConsumerType.
init_China_parameters['Nagents'] = 10000
### Import and initialize the HARK ConsumerType we want
### 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 ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# 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 income distribution. That will be done later, because we want to examine
# the effects of different income distributions.
ChinaExample.assignParameters(PermGroFac = [np.array([1.,1.06 ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #need 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)
####################################################################################################
####################################################################################################
"""
# One other parameter 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.
# It is important to note that we need to change this value here, before we have used the parameters
# to initialize the MarkovConsumerType. This is 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 init_China_parameters_dictionary; however,
# they can also be changed later, by altering the appropriate attribute of the initialized
# MarkovConsumerType.
init_China_parameters['AgentCount'] = 10000
### Import and initialize the HARK ConsumerType we want
### 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 ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# 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 income distribution. That will be done later, because we want to examine
# the effects of different income distributions.
ChinaExample.assignParameters(PermGroFac = [np.array([1.,1.06 ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #need 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
####################################################################################################
####################################################################################################
"""
"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)
MarkovType.solution_terminal.vPPfunc = 2*[MarkovType.solution_terminal.vPPfunc]
MarkovType.solution_terminal.mNrmMin = 2*[MarkovType.solution_terminal.mNrmMin]
MarkovType.solution_terminal.MPCmax = np.array(2*[MarkovType.solution_terminal.MPCmax])
MarkovType.IncomeDstn = [[employed_income_dist,unemployed_income_dist]]
MarkovType.MrkvArray = MrkvArray
MarkovType.time_inv.append('MrkvArray')
MarkovType.solveOnePeriod = solveConsumptionSavingMarkov
MarkovType.cycles = 0
MarkovType.solve()
MarkovType.unpack_cFunc()
=======
'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()
>>>>>>> eeb37f24755d0c683c9d9efbe5e7447425c98b86
self.TBSType = TBSType
self.MarkovType = MarkovType
def test_consumption(self):
# Now compare the consumption functions and make sure they are "close"
