Skip to content

Commit

Permalink
Merge branch 'master' into i583-bl
Browse files Browse the repository at this point in the history
  • Loading branch information
@llorracc
llorracc authored Apr 16, 2020
2 parents 21f0c7e + f5784d5 commit 17138dc
Show file tree
Hide file tree
Showing 21 changed files with 1,317 additions and 689 deletions.
60 changes: 39 additions & 21 deletions HARK/ConsumptionSaving/ConsAggShockModel.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -10,12 +10,12 @@
from builtins import range
import numpy as np
import scipy.stats as stats
from HARK.distribution import DiscreteDistribution, combineIndepDstns, approxMeanOneLognormal
from HARK.interpolation import LinearInterp, LinearInterpOnInterp1D, ConstantFunction, IdentityFunction,\
VariableLowerBoundFunc2D, BilinearInterp, LowerEnvelope2D, UpperEnvelope
from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv,\
CRRAutility_invP, CRRAutility_inv, combineIndepDstns,\
approxMeanOneLognormal
from HARK.simulation import drawDiscrete, drawUniform
CRRAutility_invP, CRRAutility_inv
from HARK.simulation import drawUniform
from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks
from HARK import HARKobject, Market, AgentType
from copy import deepcopy
Expand Down Expand Up @@ -416,15 +416,18 @@ def addAggShkDstn(self, AggShkDstn):
state. AggShkDstn is a list of aggregate productivity shock distributions
for each Markov state.
'''
if len(self.IncomeDstn[0][0]) > 3:
if len(self.IncomeDstn[0][0].X) > 2:
self.IncomeDstn = self.IncomeDstnWithoutAggShocks
else:
self.IncomeDstnWithoutAggShocks = self.IncomeDstn

IncomeDstnOut = []
N = self.MrkvArray.shape[0]
for t in range(self.T_cycle):
IncomeDstnOut.append([combineIndepDstns(self.IncomeDstn[t][n], AggShkDstn[n]) for n in range(N)])
IncomeDstnOut.append(
[combineIndepDstns(self.IncomeDstn[t][n],
AggShkDstn[n])
for n in range(N)])
self.IncomeDstn = IncomeDstnOut

def updateSolutionTerminal(self):
Expand Down Expand Up @@ -475,8 +478,12 @@ def getShocks(self):
PermGroFacNow = self.PermGroFac[t-1] # and permanent growth factor
Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers
# Get random draws of income shocks from the discrete distribution
EventDraws = drawDiscrete(N, X=Indices, P=IncomeDstnNow[0],
exact_match=True, seed=self.RNG.randint(0, 2**31-1))
EventDraws = DiscreteDistribution(
IncomeDstnNow[0],
Indices
).drawDiscrete(N,
exact_match=True,
seed=self.RNG.randint(0, 2**31-1))
# permanent "shock" includes expected growth
PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow
TranShkNow[these] = IncomeDstnNow[2][EventDraws]
Expand All @@ -490,8 +497,10 @@ def getShocks(self):
PermGroFacNow = self.PermGroFac[0] # and permanent growth factor
Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers
# Get random draws of income shocks from the discrete distribution
EventDraws = drawDiscrete(N, X=Indices, P=IncomeDstnNow[0],
exact_match=False, seed=self.RNG.randint(0, 2**31-1))
EventDraws = DiscreteDistribution(
IncomeDstnNow[0], Indices
).drawDiscrete(N,
seed=self.RNG.randint(0, 2**31-1))
# permanent "shock" includes expected growth
PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow
TranShkNow[these] = IncomeDstnNow[2][EventDraws]
Expand Down Expand Up @@ -1121,10 +1130,13 @@ def makeAggShkHist(self):
None
'''
sim_periods = self.act_T
Events = np.arange(self.AggShkDstn[0].size) # just a list of integers
EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[0], X=Events, seed=0)
PermShkAggHist = self.AggShkDstn[1][EventDraws]
TranShkAggHist = self.AggShkDstn[2][EventDraws]
Events = np.arange(self.AggShkDstn.pmf.size) # just a list of integers
EventDraws = self.AggShkDstn.drawDiscrete(
N=sim_periods,
X=Events,
seed=0)
PermShkAggHist = self.AggShkDstn.X[0][EventDraws]
TranShkAggHist = self.AggShkDstn.X[1][EventDraws]

# Store the histories
self.PermShkAggHist = PermShkAggHist*self.PermGroFacAgg
Expand Down Expand Up @@ -1352,10 +1364,13 @@ def makeAggShkHist(self):
None
'''
sim_periods = self.act_T
Events = np.arange(self.AggShkDstn[0].size) # just a list of integers
EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[0], X=Events, seed=0)
PermShkAggHist = self.AggShkDstn[1][EventDraws]
TranShkAggHist = self.AggShkDstn[2][EventDraws]
Events = np.arange(self.AggShkDstn.pmf.size) # just a list of integers
EventDraws = self.AggShkDstn.drawDiscrete(
N=sim_periods,
X=Events,
seed=0)
PermShkAggHist = self.AggShkDstn.X[0][EventDraws]
TranShkAggHist = self.AggShkDstn.X[1][EventDraws]

# Store the histories
self.PermShkAggHist = PermShkAggHist
Expand Down Expand Up @@ -1543,10 +1558,13 @@ def makeAggShkHist(self):
PermShkAggHistAll = np.zeros((StateCount, sim_periods))
TranShkAggHistAll = np.zeros((StateCount, sim_periods))
for i in range(StateCount):
Events = np.arange(self.AggShkDstn[i][0].size) # just a list of integers
EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[i][0], X=Events, seed=0)
PermShkAggHistAll[i, :] = self.AggShkDstn[i][1][EventDraws]
TranShkAggHistAll[i, :] = self.AggShkDstn[i][2][EventDraws]
Events = np.arange(self.AggShkDstn[i].pmf.size) # just a list of integers
EventDraws = self.AggShkDstn[i].drawDiscrete(
N=sim_periods,
X=Events,
seed=0)
PermShkAggHistAll[i, :] = self.AggShkDstn[i].X[0][EventDraws]
TranShkAggHistAll[i, :] = self.AggShkDstn[i].X[1][EventDraws]

# Select the actual history of aggregate shocks based on the sequence
# of Markov states that the economy experiences
Expand Down
14 changes: 9 additions & 5 deletions HARK/ConsumptionSaving/ConsGenIncProcessModel.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -11,12 +11,13 @@
from copy import deepcopy
import numpy as np
from HARK import AgentType, HARKobject
from HARK.distribution import DiscreteDistribution
from HARK.interpolation import LowerEnvelope2D, BilinearInterp, VariableLowerBoundFunc2D, \
LinearInterpOnInterp1D, LinearInterp, CubicInterp, UpperEnvelope
from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \
CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP,\
getPercentiles
from HARK.simulation import drawLognormal, drawDiscrete, drawUniform
from HARK.simulation import drawLognormal, drawUniform
from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks

__all__ = ['ValueFunc2D', 'MargValueFunc2D', 'MargMargValueFunc2D', 'pLvlFuncAR1',
Expand Down Expand Up @@ -1119,8 +1120,9 @@ def updatepLvlGrid(self):
# Calculate distribution of persistent income in each period of lifecycle
for t in range(len(self.PermShkStd)):
if t > 0:
PermShkNow = drawDiscrete(N=self.AgentCount, P=self.PermShkDstn[t-1][0],
X=self.PermShkDstn[t-1][1], exact_match=False, seed=t)
PermShkNow = self.PermShkDstn[t-1].drawDiscrete(
N=self.AgentCount,
seed=t)
pLvlNow = self.pLvlNextFunc[t-1](pLvlNow)*PermShkNow
pLvlGrid.append(getPercentiles(pLvlNow, percentiles=self.pLvlPctiles))

Expand All @@ -1136,10 +1138,12 @@ def updatepLvlGrid(self):
pLvlNow[who_dies] = drawLognormal(np.sum(who_dies), mu=self.pLvlInitMean,
sigma=self.pLvlInitStd, seed=t+92615)
t_cycle[who_dies] = 0

for j in range(self.T_cycle): # Update persistent income
these = t_cycle == j
PermShkTemp = drawDiscrete(N=np.sum(these), P=self.PermShkDstn[j][0],
X=self.PermShkDstn[j][1], exact_match=False, seed=t+13*j)
PermShkTemp = self.PermShkDstn[j].drawDiscrete(
N=np.sum(these),
seed=t+13*j)
pLvlNow[these] = self.pLvlNextFunc[j](pLvlNow[these])*PermShkTemp
t_cycle = t_cycle + 1
t_cycle[t_cycle == self.T_cycle] = 0
Expand Down
54 changes: 31 additions & 23 deletions HARK/ConsumptionSaving/ConsIndShockModel.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,9 @@
from HARK import AgentType, Solution, NullFunc, HARKobject
from HARK.utilities import warnings # Because of "patch" to warnings modules
from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
from HARK.simulation import drawDiscrete, drawLognormal, drawUniform
from HARK.utilities import approxMeanOneLognormal, addDiscreteOutcomeConstantMean,\
combineIndepDstns, makeGridExpMult, CRRAutility, CRRAutilityP, \
from HARK.simulation import drawLognormal, drawUniform
from HARK.distribution import DiscreteDistribution, approxMeanOneLognormal, addDiscreteOutcomeConstantMean, combineIndepDstns
from HARK.utilities import makeGridExpMult, CRRAutility, CRRAutilityP, \
CRRAutilityPP, CRRAutilityP_inv, CRRAutility_invP, CRRAutility_inv, \
CRRAutilityP_invP

Expand Down Expand Up @@ -768,11 +768,10 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac):
----------
solution_next : ConsumerSolution
The solution to next period's one period problem.
IncomeDstn : [np.array]
A list containing three arrays of floats, representing a discrete
approximation to the income process between the period being solved
and the one immediately following (in solution_next). Order: event
probabilities, permanent shocks, transitory shocks.
IncomeDstn : distribution.DiscreteDistribution
A DiscreteDistribution with a pmf
and two point value arrays in X, order:
permanent shocks, transitory shocks.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
Expand All @@ -783,10 +782,13 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac):
-------
None
'''
if isinstance(IncomeDstn, list):
import pdb; pdb.set_trace()

self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor
self.ShkPrbsNext = IncomeDstn[0]
self.PermShkValsNext = IncomeDstn[1]
self.TranShkValsNext = IncomeDstn[2]
self.ShkPrbsNext = IncomeDstn.pmf
self.PermShkValsNext = IncomeDstn.X[0]
self.TranShkValsNext = IncomeDstn.X[1]
self.PermShkMinNext = np.min(self.PermShkValsNext)
self.TranShkMinNext = np.min(self.TranShkValsNext)
self.vPfuncNext = solution_next.vPfunc
Expand Down Expand Up @@ -2157,11 +2159,12 @@ def getShocks(self):
if N > 0:
IncomeDstnNow = self.IncomeDstn[t-1] # set current income distribution
PermGroFacNow = self.PermGroFac[t-1] # and permanent growth factor
Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers
# Get random draws of income shocks from the discrete distribution
EventDraws = drawDiscrete(N,X=Indices,P=IncomeDstnNow[0],exact_match=False,seed=self.RNG.randint(0,2**31-1))
PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
TranShkNow[these] = IncomeDstnNow[2][EventDraws]
EventDraws = IncomeDstnNow.draw_events(N,
seed=self.RNG.randint(0,2**31-1))

PermShkNow[these] = IncomeDstnNow.X[0][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
TranShkNow[these] = IncomeDstnNow.X[1][EventDraws]

# That procedure used the *last* period in the sequence for newborns, but that's not right
# Redraw shocks for newborns, using the *first* period in the sequence. Approximation.
Expand All @@ -2170,11 +2173,12 @@ def getShocks(self):
these = newborn
IncomeDstnNow = self.IncomeDstn[0] # set current income distribution
PermGroFacNow = self.PermGroFac[0] # and permanent growth factor
Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers

# Get random draws of income shocks from the discrete distribution
EventDraws = drawDiscrete(N,X=Indices,P=IncomeDstnNow[0],exact_match=False,seed=self.RNG.randint(0,2**31-1))
PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
TranShkNow[these] = IncomeDstnNow[2][EventDraws]
EventDraws = IncomeDstnNow.draw_events(N,
seed=self.RNG.randint(0,2**31-1))
PermShkNow[these] = IncomeDstnNow.X[0][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
TranShkNow[these] = IncomeDstnNow.X[1][EventDraws]
# PermShkNow[newborn] = 1.0
TranShkNow[newborn] = 1.0

Expand Down Expand Up @@ -2375,7 +2379,8 @@ def checkFVAC(self,verbose,public_call):
Evaluate and report on the Finite Value of Autarky Condition
Hyperlink to paper: [url]/#Autarky-Value
'''
EpShkuInv = np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA))
EpShkuInv = np.dot(self.PermShkDstn[0].pmf,
self.PermShkDstn[0].X**(1-self.CRRA))
if self.CRRA != 1.0:
uInvEpShkuInv = EpShkuInv**(1/(1-self.CRRA)) # The term that gives a utility-consequence-adjusted utility growth
else:
Expand Down Expand Up @@ -2431,8 +2436,9 @@ def checkConditions(self,verbose=False,public_call=True):
# [url]/#Uncertainty-Modified-Conditions

self.InvPermShkDstn=deepcopy(self.PermShkDstn)
self.InvPermShkDstn[0][1]=1/self.PermShkDstn[0][1]
EPermShkInv=np.dot(self.InvPermShkDstn[0][0],1/self.PermShkDstn[0][1]) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec)
self.InvPermShkDstn[0].X = 1/self.PermShkDstn[0].X
EPermShkInv=np.dot(self.InvPermShkDstn[0].pmf,
1/self.PermShkDstn[0].X) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec)

InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version)
PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj
Expand Down Expand Up @@ -2782,7 +2788,9 @@ def constructLognormalIncomeProcessUnemployment(parameters):
PermShkValsRet = np.array([1.0])
TranShkValsRet = np.array([1.0])
ShkPrbsRet = np.array([1.0])
IncomeDstnRet = [ShkPrbsRet,PermShkValsRet,TranShkValsRet]
IncomeDstnRet = DiscreteDistribution(ShkPrbsRet,
[PermShkValsRet,
TranShkValsRet])

# Loop to fill in the list of IncomeDstn random variables.
for t in range(T_cycle): # Iterate over all periods, counting forward
Expand Down
Loading

0 comments on commit 17138dc

Please sign in to comment.