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[WIP] use PrefShkDstn more widely in ConsPrefShockModel #642

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[WIP] use PrefShkDstn more widely in ConsPrefShockModel #642

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24 changes: 11 additions & 13 deletions HARK/ConsumptionSaving/ConsPrefShockModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,8 +314,7 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA,
'''
ConsIndShockSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,
Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool)
self.PrefShkPrbs = PrefShkDstn.pmf
self.PrefShkVals = PrefShkDstn.X
self.PrefShkDstn = PrefShkDstn

def getPointsForInterpolation(self,EndOfPrdvP,aNrmNow):
'''
Expand All @@ -338,8 +337,8 @@ def getPointsForInterpolation(self,EndOfPrdvP,aNrmNow):
Corresponding market resource points for interpolation.
'''
c_base = self.uPinv(EndOfPrdvP)
PrefShkCount = self.PrefShkVals.size
PrefShk_temp = np.tile(np.reshape(self.PrefShkVals**(1.0/self.CRRA),(PrefShkCount,1)),
PrefShkCount = self.PrefShkDstn.X.size
PrefShk_temp = np.tile(np.reshape(self.PrefShkDstn.X**(1.0/self.CRRA),(PrefShkCount,1)),
(1,c_base.size))
self.cNrmNow = np.tile(c_base,(PrefShkCount,1))*PrefShk_temp
self.mNrmNow = self.cNrmNow + np.tile(aNrmNow,(PrefShkCount,1))
Expand Down Expand Up @@ -371,23 +370,23 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator):
consumption function, marginal value function, and minimum m.
'''
# Make the preference-shock specific consumption functions
PrefShkCount = self.PrefShkVals.size
PrefShkCount = self.PrefShkDstn.pmf.size
cFunc_list = []
for j in range(PrefShkCount):
MPCmin_j = self.MPCminNow*self.PrefShkVals[j]**(1.0/self.CRRA)
MPCmin_j = self.MPCminNow*self.PrefShkDstn.X[j]**(1.0/self.CRRA)
cFunc_this_shock = LowerEnvelope(LinearInterp(mNrm[j,:],cNrm[j,:],
intercept_limit=self.hNrmNow*MPCmin_j,
slope_limit=MPCmin_j),self.cFuncNowCnst)
cFunc_list.append(cFunc_this_shock)

# Combine the list of consumption functions into a single interpolation
cFuncNow = LinearInterpOnInterp1D(cFunc_list,self.PrefShkVals)
cFuncNow = LinearInterpOnInterp1D(cFunc_list,self.PrefShkDstn.X)

# Make the ex ante marginal value function (before the preference shock)
m_grid = self.aXtraGrid + self.mNrmMinNow
vP_vec = np.zeros_like(m_grid)
for j in range(PrefShkCount): # numeric integration over the preference shock
vP_vec += self.uP(cFunc_list[j](m_grid))*self.PrefShkPrbs[j]*self.PrefShkVals[j]
vP_vec += self.uP(cFunc_list[j](m_grid))*self.PrefShkDstn.pmf[j]*self.PrefShkDstn.X[j]
vPnvrs_vec = self.uPinv(vP_vec)
vPfuncNow = MargValueFunc(LinearInterp(m_grid,vPnvrs_vec),self.CRRA)

Expand All @@ -413,13 +412,13 @@ def makevFunc(self,solution):
'''
# Compute expected value and marginal value on a grid of market resources,
# accounting for all of the discrete preference shocks
PrefShkCount = self.PrefShkVals.size
PrefShkCount = self.PrefShkDstn.pmf.size
mNrm_temp = self.mNrmMinNow + self.aXtraGrid
vNrmNow = np.zeros_like(mNrm_temp)
vPnow = np.zeros_like(mNrm_temp)
for j in range(PrefShkCount):
this_shock = self.PrefShkVals[j]
this_prob = self.PrefShkPrbs[j]
this_shock = self.PrefShkDstn.X[j]
this_prob = self.PrefShkDstn.pmf[j]
cNrmNow = solution.cFunc(mNrm_temp,this_shock*np.ones_like(mNrm_temp))
aNrmNow = mNrm_temp - cNrmNow
vNrmNow += this_prob*(this_shock*self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow))
Expand Down Expand Up @@ -563,8 +562,7 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA,
'''
ConsKinkedRsolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,
Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool)
self.PrefShkPrbs = PrefShkDstn.pmf
self.PrefShkVals = PrefShkDstn.X
self.PrefShkDstn = PrefShkDstn


def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn,
Expand Down