def getEconomyData(self,Economy):
'''
Imports economy-determined objects into self from a Market.
Instances of AggShockConsumerType "live" in some macroeconomy that has
attributes relevant to their microeconomic model, like the relationship
between the capital-to-labor ratio and the interest and wage rates; this
method imports those attributes from an "economy" object and makes them
attributes of the ConsumerType.
Parameters
----------
Economy : Market
The "macroeconomy" in which this instance "lives". Might be of the
subclass CobbDouglasEconomy, which has methods to generate the
relevant attributes.
Returns
-------
None
'''
self.a_init = Economy.KtoYSS*np.ones(self.Nagents) # Initialize assets to steady state
self.kGrid = Economy.kSS*self.kGridBase # Capital ratio grid adjusted around SS ratio
self.kNextFunc = Economy.kNextFunc # Next period's capital ratio as function of current ratio
self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio
self.wFunc = Economy.wFunc # (Normalized) wage rate as function of capital ratio
IncomeDstnWithAggShks = combineIndepDstns(self.PermShkDstn,self.TranShkDstn,Economy.PermShkAggDstn) #Economy.TranShkAggDstn
self.IncomeDstn = [IncomeDstnWithAggShks] # Discrete income distribution with aggregate and idiosyncratic shocks
self.DiePrb = 1.0 - self.LivPrb[0] # Only relevant for simulating with mortality
self.addToTimeInv('kGrid','kNextFunc','Rfunc', 'wFunc')
def update(self):
'''
Use primitive parameters (and perfect foresight calibrations) to make
interest factor and wage rate functions (of capital to labor ratio),
as well as discrete approximations to the aggregate shock distributions.
Parameters
----------
none
Returns
-------
none
'''
self.kSS = ((self.CRRA/self.DiscFac - (1.0-self.DeprFac))/self.CapShare)**(1.0/(self.CapShare-1.0))
self.KtoYSS = self.kSS**(1.0-self.CapShare)
self.wRteSS = (1.0-self.CapShare)*self.kSS**(self.CapShare)
self.convertKtoY = lambda KtoY : KtoY**(1.0/(1.0 - self.CapShare)) # converts K/Y to K/L
self.Rfunc = lambda k : (1.0 + self.CapShare*k**(self.CapShare-1.0) - self.DeprFac)
self.wFunc = lambda k : ((1.0-self.CapShare)*k**(self.CapShare))/self.wRteSS
self.KtoLnow_init = self.kSS
self.RfreeNow_init = self.Rfunc(self.kSS)
self.wRteNow_init = self.wFunc(self.kSS)
self.PermShkAggNow_init = 1.0
self.TranShkAggNow_init = 1.0
self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,N=self.TranShkAggCount)
self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,N=self.PermShkAggCount)
self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,self.TranShkAggDstn)
self.kNextFunc = CapitalEvoRule(self.intercept_prev,self.slope_prev)
def getEconomyData(self, Economy):
'''
Imports economy-determined objects into self from a Market.
Instances of AggShockConsumerType "live" in some macroeconomy that has
attributes relevant to their microeconomic model, like the relationship
between the capital-to-labor ratio and the interest and wage rates; this
method imports those attributes from an "economy" object and makes them
attributes of the ConsumerType.
Parameters
----------
Economy : Market
The "macroeconomy" in which this instance "lives". Might be of the
subclass CobbDouglasEconomy, which has methods to generate the
relevant attributes.
Returns
-------
None
'''
self.a_init = Economy.KtoYSS * np.ones(
self.Nagents) # Initialize assets to steady state
self.kGrid = Economy.kSS * self.kGridBase # Capital ratio grid adjusted around SS ratio
self.kNextFunc = Economy.kNextFunc # Next period's capital ratio as function of current ratio
self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio
self.wFunc = Economy.wFunc # (Normalized) wage rate as function of capital ratio
IncomeDstnWithAggShks = combineIndepDstns(
self.PermShkDstn, self.TranShkDstn,
Economy.PermShkAggDstn) #Economy.TranShkAggDstn
self.IncomeDstn = [
IncomeDstnWithAggShks
] # Discrete income distribution with aggregate and idiosyncratic shocks
self.DiePrb = 1.0 - self.LivPrb[
0] # Only relevant for simulating with mortality
self.addToTimeInv('kGrid', 'kNextFunc', 'Rfunc', 'wFunc')
def update(self):
'''
Use primitive parameters to set basic objects. This is an extremely stripped-down version
of update for CobbDouglasEconomy.
Parameters
----------
none
Returns
-------
none
'''
self.kSS = 1.0
self.MSS = 1.0
self.KtoLnow_init = self.kSS
self.Rfunc = ConstantFunction(self.Rfree)
self.wFunc = ConstantFunction(self.wRte)
self.RfreeNow_init = self.Rfunc(self.kSS)
self.wRteNow_init = self.wFunc(self.kSS)
self.MaggNow_init = self.kSS
self.AaggNow_init = self.kSS
self.PermShkAggNow_init = 1.0
self.TranShkAggNow_init = 1.0
self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,
N=self.TranShkAggCount)
self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,
N=self.PermShkAggCount)
self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,
self.TranShkAggDstn)
self.AFunc = ConstantFunction(1.0)
def updateIncomeProcess(self):
'''
An alternative method for constructing the income process in the infinite horizon model.
Parameters
----------
none
Returns
-------
none
'''
if self.cycles == 0:
tax_rate = (self.IncUnemp * self.UnempPrb) / (
(1.0 - self.UnempPrb) * self.IndL)
TranShkDstn = deepcopy(
approxMeanOneLognormal(self.TranShkCount,
sigma=self.TranShkStd[0],
tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0] * (1.0 - self.UnempPrb),
0, self.UnempPrb)
TranShkDstn[1] = np.insert(
TranShkDstn[1] * (1.0 - tax_rate) * self.IndL, 0,
self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,
sigma=self.PermShkStd[0],
tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn, TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
else: # Do the usual method if this is the lifecycle model
EstimationAgentClass.updateIncomeProcess(self)
def update(self):
'''
Use primitive parameters to set basic objects. This is an extremely stripped-down version
of update for CobbDouglasEconomy.
Parameters
----------
none
Returns
-------
none
'''
self.kSS = 1.0
self.MSS = 1.0
self.KtoLnow_init = self.kSS
self.Rfunc = ConstantFunction(self.Rfree)
self.wFunc = ConstantFunction(self.wRte)
self.RfreeNow_init = self.Rfunc(self.kSS)
self.wRteNow_init = self.wFunc(self.kSS)
self.MaggNow_init = self.kSS
self.AaggNow_init = self.kSS
self.PermShkAggNow_init = 1.0
self.TranShkAggNow_init = 1.0
self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,N=self.TranShkAggCount)
self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,N=self.PermShkAggCount)
self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,self.TranShkAggDstn)
self.AFunc = ConstantFunction(1.0)
def updateIncomeProcessAlt(self):
'''
An alternative method for constructing the income process in the infinite
horizon model, where the labor supply l_bar creates a small oddity.
Parameters
----------
none
Returns
-------
none
'''
tax_rate = (self.IncUnemp * self.UnempPrb) / (self.l_bar *
(1.0 - self.UnempPrb))
TranShkDstn = deepcopy(
approxMeanOneLognormal(self.TranShkCount,
sigma=self.TranShkStd[0],
tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0] * (1.0 - self.UnempPrb), 0,
self.UnempPrb)
TranShkDstn[1] = np.insert(
self.l_bar * TranShkDstn[1] * (1.0 - tax_rate), 0, self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,
sigma=self.PermShkStd[0],
tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn, TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
def getEconomyData(self,Economy):
'''
Imports economy-determined objects into self from a Market.
Instances of AggShockConsumerType "live" in some macroeconomy that has
attributes relevant to their microeconomic model, like the relationship
between the capital-to-labor ratio and the interest and wage rates; this
method imports those attributes from an "economy" object and makes them
attributes of the ConsumerType.
Parameters
----------
Economy : Market
The "macroeconomy" in which this instance "lives". Might be of the
subclass CobbDouglasEconomy, which has methods to generate the
relevant attributes.
Returns
-------
None
'''
self.kInit = Economy.kSS # Initialize simulation assets to steady state
self.aNrmInitMean = np.log(0.00000001) # Initialize newborn assets to nearly zero
self.Mgrid = Economy.MSS*self.MgridBase # Aggregate market resources grid adjusted around SS capital ratio
self.AFunc = Economy.AFunc # Next period's aggregate savings function
self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio
self.wFunc = Economy.wFunc # Wage rate as function of capital ratio
self.DeprFac = Economy.DeprFac # Rate of capital depreciation
IncomeDstnWithAggShks = combineIndepDstns(self.PermShkDstn,self.TranShkDstn,Economy.PermShkAggDstn,Economy.TranShkAggDstn)
self.IncomeDstn = [IncomeDstnWithAggShks] # Discrete income distribution with aggregate and idiosyncratic shocks
self.addToTimeInv('Mgrid','AFunc','Rfunc', 'wFunc','DeprFac')
def update(self):
'''
Use primitive parameters (and perfect foresight calibrations) to make
interest factor and wage rate functions (of capital to labor ratio),
as well as discrete approximations to the aggregate shock distributions.
Parameters
----------
none
Returns
-------
none
'''
self.kSS = ((1.0 / self.DiscFac - (1.0 - self.DeprFac)) /
self.CapShare)**(1.0 / (self.CapShare - 1.0))
self.KtoYSS = self.kSS**(1.0 - self.CapShare)
self.wRteSS = (1.0 - self.CapShare) * self.kSS**(self.CapShare)
self.RfreeSS = (1.0 + self.CapShare * self.kSS**(self.CapShare - 1.0) -
self.DeprFac)
self.MSS = self.kSS * (self.RfreeSS + self.DeprFac) + self.wRteSS
self.convertKtoY = lambda KtoY: KtoY**(1.0 / (1.0 - self.CapShare)
) # converts K/Y to K/L
self.Rfunc = lambda k: (1.0 + self.CapShare * k**
(self.CapShare - 1.0) - self.DeprFac)
self.wFunc = lambda k: ((1.0 - self.CapShare) * k**(self.CapShare))
self.KtoLnow_init = self.kSS
self.MaggNow_init = self.kSS
self.AaggNow_init = self.kSS
self.RfreeNow_init = self.Rfunc(self.kSS)
self.wRteNow_init = self.wFunc(self.kSS)
self.PermShkAggNow_init = 1.0
self.TranShkAggNow_init = 1.0
self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,
N=self.TranShkAggCount)
self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,
N=self.PermShkAggCount)
self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,
self.TranShkAggDstn)
self.AFunc = AggregateSavingRule(self.intercept_prev, self.slope_prev)
def updateIncomeProcessAlt(self):
'''
An alternative method for constructing the income process in the infinite
horizon model, where the labor supply l_bar creates a small oddity.
Parameters
----------
none
Returns
-------
none
'''
tax_rate = (self.IncUnemp*self.UnempPrb)/(self.l_bar*(1.0-self.UnempPrb))
TranShkDstn = deepcopy(approxMeanOneLognormal(self.TranShkCount,sigma=self.TranShkStd[0],tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0]*(1.0-self.UnempPrb),0,self.UnempPrb)
TranShkDstn[1] = np.insert(self.l_bar*TranShkDstn[1]*(1.0-tax_rate),0,self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,sigma=self.PermShkStd[0],tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn,TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
def getEconomyData(self, Economy):
'''
Imports economy-determined objects into self from a Market.
Instances of AggShockConsumerType "live" in some macroeconomy that has
attributes relevant to their microeconomic model, like the relationship
between the capital-to-labor ratio and the interest and wage rates; this
method imports those attributes from an "economy" object and makes them
attributes of the ConsumerType.
Parameters
----------
Economy : Market
The "macroeconomy" in which this instance "lives". Might be of the
subclass CobbDouglasEconomy, which has methods to generate the
relevant attributes.
Returns
-------
None
'''
self.kInit = Economy.kSS # Initialize simulation assets to steady state
self.aNrmInitMean = np.log(
0.00000001) # Initialize newborn assets to nearly zero
self.Mgrid = Economy.MSS * self.MgridBase # Aggregate market resources grid adjusted around SS capital ratio
self.AFunc = Economy.AFunc # Next period's aggregate savings function
self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio
self.wFunc = Economy.wFunc # Wage rate as function of capital ratio
self.DeprFac = Economy.DeprFac # Rate of capital depreciation
IncomeDstnWithAggShks = combineIndepDstns(self.PermShkDstn,
self.TranShkDstn,
Economy.PermShkAggDstn,
Economy.TranShkAggDstn)
self.IncomeDstn = [
IncomeDstnWithAggShks
] # Discrete income distribution with aggregate and idiosyncratic shocks
self.addToTimeInv('Mgrid', 'AFunc', 'Rfunc', 'wFunc', 'DeprFac')
def updateIncomeProcess(self):
'''
An alternative method for constructing the income process in the infinite horizon model.
Parameters
----------
none
Returns
-------
none
'''
if self.cycles == 0:
tax_rate = (self.IncUnemp*self.UnempPrb)/((1.0-self.UnempPrb)*self.IndL)
TranShkDstn = deepcopy(approxMeanOneLognormal(self.TranShkCount,sigma=self.TranShkStd[0],tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0]*(1.0-self.UnempPrb),0,self.UnempPrb)
TranShkDstn[1] = np.insert(TranShkDstn[1]*(1.0-tax_rate)*self.IndL,0,self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,sigma=self.PermShkStd[0],tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn,TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
else: # Do the usual method if this is the lifecycle model
EstimationAgentClass.updateIncomeProcess(self)
def constructLognormalIncomeAndPreferenceProcess(parameters):
'''
Generates a list of discrete approximations to the income and preference shock
process . Permanent shocks are mean one lognormally distributed
with standard deviation PermShkStd. Transitory shocks
are mean one lognormally distributed
Parameters (passed as attributes of the input parameters)
----------
PermShkStd : [float]
List of standard deviations in log permanent income uncertainty during
the agent's life.
PermShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the permanent income shock distribution.
TranShkStd : [float]
List of standard deviations in log transitory income uncertainty during
the agent's life.
TranShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the permanent income shock distribution.
PrefShkStd : [float]
List of standard deviations in log preference uncertainty during
the agent's life.
PrefShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the preference shock distribution.
Returns
-------
IncomeAndPrefDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of four arrays
representing a discrete approximation to the income and preference
process in a period.
Order: probabilities, permanent shocks, transitory shocks, preference shocks.
PermShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the permanent income shocks.
TranShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the transitory income shocks.
PrefShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the preference shocks.
'''
# Unpack the parameters from the input
PermShkStd = parameters.PermShkStd
PermShkCount = parameters.PermShkCount
TranShkStd = parameters.TranShkStd
TranShkCount = parameters.TranShkCount
PrefShkStd = parameters.PrefShkStd
PrefShkCount = parameters.PrefShkCount
T_cycle = 1
UnempPrb = parameters.UnempPrb
IncUnemp = parameters.IncUnemp
IncomeDstn = [] # Discrete approximations to income process in each period
PermShkDstn = [] # Discrete approximations to permanent income shocks
TranShkDstn = [] # Discrete approximations to transitory income shocks
PrefShkDstn = [] # Discrete approximations to preference shocks
t = 0
TranShkDstn_t = approxMeanOneLognormal(N=TranShkCount,
sigma=TranShkStd[t],
tail_N=0)
if UnempPrb > 0:
TranShkDstn_t = addDiscreteOutcomeConstantMean(TranShkDstn_t,
p=UnempPrb,
x=IncUnemp)
#add in a shock at zero to impose natural borrowing constraint
TranShkDstn_t = addDiscreteOutcomeConstantMean(TranShkDstn_t,
p=0.000000001,
x=0.0)
PermShkDstn_t = approxMeanOneLognormal(N=PermShkCount,
sigma=PermShkStd[t],
tail_N=0)
PrefShkDstn_t = approxMeanOneLognormal(N=PrefShkCount,
sigma=PrefShkStd[t],
tail_N=0)
IncomeDstn.append(
combineIndepDstns(PermShkDstn_t, TranShkDstn_t,
PrefShkDstn_t)) # mix the independent distributions
PermShkDstn.append(PermShkDstn_t)
TranShkDstn.append(TranShkDstn_t)
PrefShkDstn.append(PrefShkDstn_t)
return IncomeDstn, PermShkDstn, TranShkDstn, PrefShkDstn
Parameters
----------
none
Returns
-------
none
'''
tax_rate = (self.IncUnemp*self.UnempPrb)/(self.l_bar*(1.0-self.UnempPrb))
<<<<<<< HEAD
TranShkDstn = deepcopy(approxLognormal(self.TranShkCount,sigma=self.TranShkStd[0],tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0]*(1.0-self.UnempPrb),0,self.UnempPrb)
TranShkDstn[1] = np.insert(self.l_bar*TranShkDstn[1]*(1.0-tax_rate),0,self.IncUnemp)
PermShkDstn = approxLognormal(self.PermShkCount,sigma=self.PermShkStd[0],tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn,TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
if not 'IncomeDstn' in self.time_vary:
self.time_vary.append('IncomeDstn')
=======
TranShkDstn = deepcopy(approxMeanOneLognormal(self.TranShkCount,sigma=self.TranShkStd[0],tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0]*(1.0-self.UnempPrb),0,self.UnempPrb)
TranShkDstn[1] = np.insert(self.l_bar*TranShkDstn[1]*(1.0-tax_rate),0,self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,sigma=self.PermShkStd[0],tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn,TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
>>>>>>> eeb37f24755d0c683c9d9efbe5e7447425c98b86
