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Quasi hyperbolic pf #833

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Quasi hyperbolic pf #833

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93c573b
Implementation of geometric_solution
g-carranza Dec 20, 2018
612e032
Default Hyperbolic_beta value
g-carranza Dec 20, 2018
6baa7c6
Introduction of hyperbolic_beta
g-carranza Dec 20, 2018
ef0c95d
Delete presolve
g-carranza Dec 21, 2018
a1e3eb1
Example of hyperbolic discounting
g-carranza Dec 21, 2018
5c11375
fix merge conflicts
MridulS Sep 3, 2020
24a967d
fix up example
MridulS Sep 3, 2020
465768d
Merge branch 'master' into quasi-hyperbolic-pf
Sep 26, 2020
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48 changes: 31 additions & 17 deletions HARK/ConsumptionSaving/ConsIndShockModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -366,6 +366,7 @@ def __init__(
PermGroFac,
BoroCnstArt,
MaxKinks,
HyperbolicBeta,
):
"""
Constructor for a new ConsPerfForesightSolver.
Expand Down Expand Up @@ -393,6 +394,9 @@ def __init__(
additional points will be thrown out. Only relevant in infinite
horizon model with artificial borrowing constraint.

HyperbolicBeta: float
Quasi hyperbolic impatience factor in "beta-delta" preferences.

Returns:
----------
Comment on lines +399 to 401

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The docstring is missing a blank line before the "Returns:" section and has inconsistent formatting with an empty line after the description. The formatting should match the project's documentation standards.

Suggested change
Returns:
----------
Returns
-------

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None
Expand All @@ -414,6 +418,7 @@ def __init__(
PermGroFac=PermGroFac,
BoroCnstArt=BoroCnstArt,
MaxKinks=MaxKinks,
HyperbolicBeta=HyperbolicBeta,
)

def defUtilityFuncs(self):
Expand Down Expand Up @@ -612,7 +617,7 @@ def solve(self):
The solution to this period's problem.
"""
self.defUtilityFuncs()
self.DiscFacEff = self.DiscFac * self.LivPrb
self.DiscFacEff = self.DiscFac * self.LivPrb * self.HyperbolicBeta

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The new HyperbolicBeta functionality lacks automated test coverage. While there is a Jupyter notebook demonstrating the feature, there are no unit tests in the test_PerfForesightConsumerType.py file that verify the quasi-hyperbolic discounting behavior. Consider adding tests that verify the consumption functions match the expected algebraic solutions when HyperbolicBeta is not equal to 1.

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self.makePFcFunc()
self.defValueFuncs()
solution = ConsumerSolution(
Expand Down Expand Up @@ -1528,21 +1533,22 @@ def prepareToCalcEndOfPrdvP(self):

# Make a dictionary to specify a perfect foresight consumer type
init_perfect_foresight = {
'CRRA': 2.0, # Coefficient of relative risk aversion,
'Rfree': 1.03, # Interest factor on assets
'DiscFac': 0.96, # Intertemporal discount factor
'LivPrb': [0.98], # Survival probability
'PermGroFac': [1.01], # Permanent income growth factor
'BoroCnstArt': None, # Artificial borrowing constraint
'MaxKinks': 400, # Maximum number of grid points to allow in cFunc (should be large)
'AgentCount': 10000, # Number of agents of this type (only matters for simulation)
'aNrmInitMean' : 0.0, # Mean of log initial assets (only matters for simulation)
'aNrmInitStd' : 1.0, # Standard deviation of log initial assets (only for simulation)
'pLvlInitMean' : 0.0, # Mean of log initial permanent income (only matters for simulation)
'pLvlInitStd' : 0.0, # Standard deviation of log initial permanent income (only matters for simulation)
'PermGroFacAgg' : 1.0,# Aggregate permanent income growth factor: portion of PermGroFac attributable to aggregate productivity growth (only matters for simulation)
'T_age' : None, # Age after which simulated agents are automatically killed
'T_cycle' : 1 # Number of periods in the cycle for this agent type
"CRRA": 2.0, # Coefficient of relative risk aversion,
"Rfree": 1.03, # Interest factor on assets
"DiscFac": 0.96, # Intertemporal discount factor
"LivPrb": [0.98], # Survival probability
"PermGroFac": [1.01], # Permanent income growth factor
"BoroCnstArt": None, # Artificial borrowing constraint
"MaxKinks": 400, # Maximum number of grid points to allow in cFunc (should be large)
"AgentCount": 10000, # Number of agents of this type (only matters for simulation)
"aNrmInitMean": 0.0, # Mean of log initial assets (only matters for simulation)
"aNrmInitStd": 1.0, # Standard deviation of log initial assets (only for simulation)
"pLvlInitMean": 0.0, # Mean of log initial permanent income (only matters for simulation)
"pLvlInitStd": 0.0, # Standard deviation of log initial permanent income (only matters for simulation)
"PermGroFacAgg": 1.0, # Aggregate permanent income growth factor (only matters for simulation)
"T_age": None, # Age after which simulated agents are automatically killed
"T_cycle": 1, # Number of periods in the cycle for this agent type
"HyperbolicBeta": 1,

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The HyperbolicBeta parameter is added to init_perfect_foresight but lacks a descriptive comment like all other parameters in the dictionary. Add a comment such as "# Quasi-hyperbolic discount factor (naive agents)" to maintain consistency.

Suggested change
"HyperbolicBeta": 1,
"HyperbolicBeta": 1, # Quasi-hyperbolic discount factor (naive agents)

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}


Expand All @@ -1566,7 +1572,15 @@ class PerfForesightConsumerType(AgentType):
MPCmax=1.0,
)
time_vary_ = ["LivPrb", "PermGroFac"]
time_inv_ = ["CRRA", "Rfree", "DiscFac", "MaxKinks", "BoroCnstArt"]
time_inv_ = [
"CRRA",
"Rfree",
"DiscFac",
"MaxKinks",
"BoroCnstArt",
"HyperbolicBeta",
"geometric_solution",

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Adding "geometric_solution" to time_inv_ makes it a required parameter that must be provided during initialization. This could break existing code that creates PerfForesightConsumerType instances without this parameter. Consider making this parameter optional by checking for its existence with hasattr() (as already done in core.py line 923) rather than requiring it as a time-invariant parameter.

Suggested change
"geometric_solution",

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]
poststate_vars_ = ["aNrmNow", "pLvlNow"]
shock_vars_ = []

Expand Down
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