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Merge pull request #527 from sbenthall/i446
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Distribute parameter dictionaries to nearby class definitions
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@mnwhite
mnwhite committed Mar 17, 2020
2 parents 76e7aee + 34c1467 commit 6631c0d
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Showing 16 changed files with 679 additions and 433 deletions.
97 changes: 82 additions & 15 deletions HARK/ConsumptionSaving/ConsAggShockModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,15 +16,13 @@
CRRAutility_invP, CRRAutility_inv, combineIndepDstns,\
approxMeanOneLognormal
from HARK.simulation import drawDiscrete, drawUniform
from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType
from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks
from HARK import HARKobject, Market, AgentType
from copy import deepcopy
# import matplotlib.pyplot as plt
import HARK.ConsumptionSaving.ConsumerParameters as Params

__all__ = ['MargValueFunc2D', 'AggShockConsumerType', 'AggShockMarkovConsumerType',
'CobbDouglasEconomy', 'SmallOpenEconomy', 'CobbDouglasMarkovEconomy',
'SmallOpenMarkovEconomy', 'CobbDouglasAggVars', 'AggregateSavingRule', 'AggShocksDynamicRule']
'SmallOpenMarkovEconomy', 'CobbDouglasAggVars', 'AggregateSavingRule', 'AggShocksDynamicRule','init_agg_shocks','init_agg_mrkv_shocks', 'init_cobb_douglas','init_mrkv_cobb_douglas']

utility = CRRAutility
utilityP = CRRAutilityP
Expand Down Expand Up @@ -68,6 +66,18 @@ def __call__(self, m, M):

###############################################################################

# Make a dictionary to specify an aggregate shocks consumer
init_agg_shocks = init_idiosyncratic_shocks.copy()
del init_agg_shocks['Rfree'] # Interest factor is endogenous in agg shocks model
del init_agg_shocks['CubicBool'] # Not supported yet for agg shocks model
del init_agg_shocks['vFuncBool'] # Not supported yet for agg shocks model
init_agg_shocks['PermGroFac'] = [1.0]
# Grid of capital-to-labor-ratios (factors)
MgridBase = np.array([0.1,0.3,0.6,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.6,2.0,3.0])
init_agg_shocks['MgridBase'] = MgridBase
init_agg_shocks['aXtraCount'] = 24
init_agg_shocks['aNrmInitStd'] = 0.0
init_agg_shocks['LivPrb'] = [0.98]

class AggShockConsumerType(IndShockConsumerType):
'''
Expand All @@ -84,11 +94,11 @@ def __init__(self, time_flow=True, **kwds):
Make a new instance of AggShockConsumerType, an extension of
IndShockConsumerType. Sets appropriate solver and input lists.
'''
params = Params.init_agg_shocks.copy()
params = init_agg_shocks.copy()
params.update(kwds)
kwds = params

AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_),
time_flow=time_flow, pseudo_terminal=False, **kwds)
time_flow=time_flow, pseudo_terminal=False, **params)

# Add consumer-type specific objects, copying to create independent versions
self.time_vary = deepcopy(IndShockConsumerType.time_vary_)
Expand Down Expand Up @@ -375,6 +385,16 @@ def makeEulerErrorFunc(self, mMax=100, approx_inc_dstn=True):
raise NotImplementedError()


# This example makes a high risk, low growth state and a low risk, high growth state
MrkvArray = np.array([[0.90,0.10],[0.04,0.96]])
PermShkAggStd = [0.012,0.006] # Standard deviation of log aggregate permanent shocks by state
TranShkAggStd = [0.006,0.003] # Standard deviation of log aggregate transitory shocks by state
PermGroFacAgg = [0.98,1.02] # Aggregate permanent income growth factor

# Make a dictionary to specify a Markov aggregate shocks consumer
init_agg_mrkv_shocks = init_agg_shocks.copy()
init_agg_mrkv_shocks['MrkvArray'] = MrkvArray

class AggShockMarkovConsumerType(AggShockConsumerType):
'''
A class for representing ex ante heterogeneous "types" of consumers who
Expand All @@ -383,7 +403,7 @@ class AggShockMarkovConsumerType(AggShockConsumerType):
state is subject to Markov-style discrete state evolution.
'''
def __init__(self, **kwds):
params = Params.init_agg_mrkv_shocks.copy()
params = init_agg_mrkv_shocks.copy()
params.update(kwds)
kwds = params
AggShockConsumerType.__init__(self, **kwds)
Expand Down Expand Up @@ -885,6 +905,45 @@ def solveConsAggMarkov(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, MrkvArr

###############################################################################

CRRA = 2.0
DiscFac = 0.96

# Parameters for a Cobb-Douglas economy
PermGroFacAgg = 1.00 # Aggregate permanent income growth factor
PermShkAggCount = 3 # Number of points in discrete approximation to aggregate permanent shock dist
TranShkAggCount = 3 # Number of points in discrete approximation to aggregate transitory shock dist
PermShkAggStd = 0.0063 # Standard deviation of log aggregate permanent shocks
TranShkAggStd = 0.0031 # Standard deviation of log aggregate transitory shocks
DeprFac = 0.025 # Capital depreciation rate
CapShare = 0.36 # Capital's share of income
DiscFacPF = DiscFac # Discount factor of perfect foresight calibration
CRRAPF = CRRA # Coefficient of relative risk aversion of perfect foresight calibration
intercept_prev = 0.0 # Intercept of aggregate savings function
slope_prev = 1.0 # Slope of aggregate savings function
verbose_cobb_douglas = True # Whether to print solution progress to screen while solving
T_discard = 200 # Number of simulated "burn in" periods to discard when updating AFunc
DampingFac = 0.5 # Damping factor when updating AFunc; puts DampingFac weight on old params, rest on new
max_loops = 20 # Maximum number of AFunc updating loops to allow


# Make a dictionary to specify a Cobb-Douglas economy
init_cobb_douglas = {'PermShkAggCount': PermShkAggCount,
'TranShkAggCount': TranShkAggCount,
'PermShkAggStd': PermShkAggStd,
'TranShkAggStd': TranShkAggStd,
'DeprFac': DeprFac,
'CapShare': CapShare,
'DiscFac': DiscFacPF,
'CRRA': CRRAPF,
'PermGroFacAgg': PermGroFacAgg,
'AggregateL':1.0,
'intercept_prev': intercept_prev,
'slope_prev': slope_prev,
'verbose': verbose_cobb_douglas,
'T_discard': T_discard,
'DampingFac': DampingFac,
'max_loops': max_loops
}

class CobbDouglasEconomy(Market):
'''
Expand Down Expand Up @@ -921,18 +980,17 @@ def __init__(self,
-------
None
'''
params = Params.init_cobb_douglas.copy()
params = init_cobb_douglas.copy()
params.update(kwds)
kwds = params

Market.__init__(self, agents=agents,
sow_vars=['MaggNow', 'AaggNow', 'RfreeNow',
'wRteNow', 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'],
reap_vars=['aLvlNow', 'pLvlNow'],
track_vars=['MaggNow', 'AaggNow'],
dyn_vars=['AFunc'],
tolerance=tolerance,
act_T=act_T)
self.assignParameters(**kwds)
act_T=act_T,**params)
self.update()

# Use previously hardcoded values for AFunc updating if not passed
Expand Down Expand Up @@ -1336,6 +1394,15 @@ def getAggShocks(self):
AggVarsNow = CobbDouglasAggVars(MaggNow, AaggNow, KtoLnow, RfreeNow, wRteNow, PermShkAggNow, TranShkAggNow)
return AggVarsNow

# Make a dictionary to specify a Markov Cobb-Douglas economy
init_mrkv_cobb_douglas = init_cobb_douglas.copy()
init_mrkv_cobb_douglas['PermShkAggStd'] = [0.012,0.006]
init_mrkv_cobb_douglas['TranShkAggStd'] = [0.006,0.003]
init_mrkv_cobb_douglas['PermGroFacAgg'] = [0.98,1.02]
init_mrkv_cobb_douglas['MrkvArray'] = MrkvArray
init_mrkv_cobb_douglas['MrkvNow_init'] = 0
init_mrkv_cobb_douglas['slope_prev'] = 2*[slope_prev]
init_mrkv_cobb_douglas['intercept_prev'] = 2*[intercept_prev]

class CobbDouglasMarkovEconomy(CobbDouglasEconomy):
'''
Expand Down Expand Up @@ -1372,10 +1439,10 @@ def __init__(self,
-------
None
'''
params = Params.init_mrkv_cobb_douglas.copy()
params = init_mrkv_cobb_douglas.copy()
params.update(kwds)
kwds = params
CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds)

CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **params)
self.sow_vars.append('MrkvNow')

def update(self):
Expand Down
56 changes: 50 additions & 6 deletions HARK/ConsumptionSaving/ConsGenIncProcessModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,12 +17,12 @@
CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP,\
getPercentiles
from HARK.simulation import drawLognormal, drawDiscrete, drawUniform
from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType
import HARK.ConsumptionSaving.ConsumerParameters as Params
from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType, init_idiosyncratic_shocks

__all__ = ['ValueFunc2D', 'MargValueFunc2D', 'MargMargValueFunc2D', 'pLvlFuncAR1',
'ConsGenIncProcessSolver', 'GenIncProcessConsumerType',
'IndShockExplicitPermIncConsumerType', 'PersistentShockConsumerType']
'IndShockExplicitPermIncConsumerType', 'PersistentShockConsumerType',
'init_explicit_perm_inc', 'init_persistent_shocks']

utility = CRRAutility
utilityP = CRRAutilityP
Expand Down Expand Up @@ -958,6 +958,20 @@ def solveConsGenIncProcess(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfr

###############################################################################

# -----------------------------------------------------------------------------
# ----- Define additional parameters for the persistent shocks model ----------
# -----------------------------------------------------------------------------

pLvlPctiles = np.concatenate(([0.001, 0.005, 0.01, 0.03], np.linspace(0.05, 0.95, num=19),[0.97, 0.99, 0.995, 0.999]))
PrstIncCorr = 0.98 # Serial correlation coefficient for permanent income

# Make a dictionary for the "explicit permanent income" idiosyncratic shocks model
init_explicit_perm_inc = init_idiosyncratic_shocks.copy()
init_explicit_perm_inc['pLvlPctiles'] = pLvlPctiles
init_explicit_perm_inc['PermGroFac'] = [1.0] # long run permanent income growth doesn't work yet
init_explicit_perm_inc['aXtraMax'] = 30
init_explicit_perm_inc['aXtraExtra'] = [0.005,0.01]

class GenIncProcessConsumerType(IndShockConsumerType):
'''
A consumer type with idiosyncratic shocks to persistent and transitory income.
Expand Down Expand Up @@ -987,12 +1001,11 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
-------
None
'''
params = Params.init_explicit_perm_inc.copy()
params = init_explicit_perm_inc.copy()
params.update(kwds)
kwds = params

# Initialize a basic ConsumerType
IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds)
IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **params)
self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income

def preSolve(self):
Expand Down Expand Up @@ -1276,6 +1289,13 @@ def updatepLvlNextFunc(self):

###############################################################################




# Make a dictionary for the "persistent idiosyncratic shocks" model
init_persistent_shocks = init_explicit_perm_inc.copy()
init_persistent_shocks['PrstIncCorr'] = PrstIncCorr

class PersistentShockConsumerType(GenIncProcessConsumerType):
'''
Type with idiosyncratic shocks to persistent ('Prst') and transitory income.
Expand All @@ -1285,6 +1305,30 @@ class PersistentShockConsumerType(GenIncProcessConsumerType):
period assets, an artificial borrowing constraint, and the AR1 correlation
coefficient for (log) persistent income.
'''

def __init__(self, cycles=0, time_flow=True, **kwds):
'''
Instantiate a new ConsumerType with given data.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
time_flow : boolean
Whether time is currently "flowing" forward for this instance.
Returns
-------
None
'''
params = init_persistent_shocks.copy()
params.update(kwds)

GenIncProcessConsumerType.__init__(self,
cycles=cycles,
time_flow=time_flow,
**params)

def updatepLvlNextFunc(self):
'''
A method that creates the pLvlNextFunc attribute as a sequence of
Expand Down
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