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[WIP] Expand ConsAggShock example notebooks #1738

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4ffd971
Begin work on AggShock notebooks
mnwhite Feb 28, 2026
87c89aa
Improved AggShockConsumerType NB
mnwhite Mar 3, 2026
20e22f2
Fleshed out AggShockMarkov notebook
mnwhite Mar 4, 2026
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131 changes: 70 additions & 61 deletions HARK/ConsumptionSaving/ConsAggShockModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@
MarkovProcess,
MeanOneLogNormal,
Uniform,
calc_expectation,
expected,
combine_indep_dstns,
)
from HARK.interpolation import (
Expand Down Expand Up @@ -188,7 +188,6 @@ def solveConsAggShock(
AFunc,
Rfunc,
wFunc,
DeprRte,
):
"""
Solve one period of a consumption-saving problem with idiosyncratic and
Expand Down Expand Up @@ -231,8 +230,6 @@ def solveConsAggShock(
The net interest factor on assets as a function of capital ratio k.
wFunc : function
The wage rate for labor as a function of capital-to-labor ratio k.
DeprRte : float
Capital Depreciation Rate

Returns
-------
Expand Down Expand Up @@ -296,9 +293,7 @@ def vPnextFunc(S, a, M):

# Compute end-of-period marginal value of assets
MaggNow = np.tile(np.reshape(Mgrid, (1, Mcount)), (aCount, 1)) # Tiled Mgrid
EndOfPrdvP = (
DiscFac * LivPrb * calc_expectation(IncShkDstn, vPnextFunc, *(aNrmNow, MaggNow))
)
EndOfPrdvP = DiscFac * LivPrb * expected(vPnextFunc, IncShkDstn, (aNrmNow, MaggNow))

# Calculate optimal consumption from each asset gridpoint and endogenous m_t gridpoint
cNrmNow = EndOfPrdvP ** (-1.0 / CRRA)
Expand Down Expand Up @@ -406,8 +401,6 @@ def solve_ConsAggMarkov(
The net interest factor on assets as a function of capital ratio k.
wFunc : function
The wage rate for labor as a function of capital-to-labor ratio k.
DeprRte : float
Capital Depreciation Rate

Returns
-------
Expand Down Expand Up @@ -765,8 +758,8 @@ def solve_KrusellSmith(
default_aXtraGrid_params = {
"aXtraMin": 0.001, # Minimum end-of-period "assets above minimum" value
"aXtraMax": 20, # Maximum end-of-period "assets above minimum" value
"aXtraNestFac": 3, # Exponential nesting factor for aXtraGrid
"aXtraCount": 24, # Number of points in the grid of "assets above minimum"
"aXtraNestFac": 2, # Exponential nesting factor for aXtraGrid
"aXtraCount": 36, # Number of points in the grid of "assets above minimum"
"aXtraExtra": None, # Additional other values to add in grid (optional)
}

Expand Down Expand Up @@ -836,7 +829,7 @@ class AggShockConsumerType(IndShockConsumerType):
"track_vars": ["aNrm", "cNrm", "mNrm", "pLvl"],
}
time_inv_ = IndShockConsumerType.time_inv_.copy()
time_inv_ += ["Mgrid", "AFunc", "Rfunc", "wFunc", "DeprRte", "PermGroFacAgg"]
time_inv_ += ["Mgrid", "AFunc", "Rfunc", "wFunc", "PermGroFacAgg"]
try:
time_inv_.remove("vFuncBool")
time_inv_.remove("CubicBool")
Expand All @@ -849,7 +842,6 @@ class AggShockConsumerType(IndShockConsumerType):
"AFunc",
"Rfunc",
"wFunc",
"DeprRte",
"PermGroFacAgg",
"AggShkDstn",
]
Expand Down Expand Up @@ -1220,7 +1212,17 @@ def make_assets_grid_KS(aMin, aMax, aCount, aNestFac):


def make_KS_transition_arrays(
aGrid, Mgrid, AFunc, LbrInd, UrateB, UrateG, ProdB, ProdG, MrkvIndArray
aGrid,
Mgrid,
AFunc,
LbrInd,
UrateB,
UrateG,
ProdB,
ProdG,
MrkvIndArray,
CapShare,
DeprRte,
):
"""
Construct the attributes ProbArray, mNextArray, MnextArray, and RnextArray,
Expand Down Expand Up @@ -1248,6 +1250,10 @@ def make_KS_transition_arrays(
TFP in the "good" aggregate state.
MrkvIndArray : np.array
Markov transition probabilities from the perspective of the individual.
CapShare : float
Capital's share of production.
DeprRte : float
Capital depreciation rate.

Returns
-------
Expand Down Expand Up @@ -1651,52 +1657,23 @@ def get_poststates(self):
###############################################################################


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
DeprRte = 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
# Damping factor when updating AFunc; puts DampingFac weight on old params, rest on new
DampingFac = 0.5
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,
"DeprRte": DeprRte,
"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,
"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
"DeprRte": 0.025, # Capital depreciation rate
"CapShare": 0.36, # Capital's share of income
"DiscFac": 0.96, # Discount factor of perfect foresight calibration
"CRRA": 2.0, # Coefficient of relative risk aversion of perfect foresight calibration
"PermGroFacAgg": 1.0, # Aggregate permanent income growth factor
"intercept_prev": 0.0, # Intercept of aggregate savings function
"slope_prev": 1.0, # Slope of aggregate savings function,
"verbose": False, # Whether to print solution progress to screen while solving
"T_discard": 200, # Number of simulated "burn in" periods to discard when updating AFunc
"DampingFac": 0.1, # Damping factor when updating AFunc
"max_loops": 20, # Maximum number of AFunc updating loops to allow
}


Expand Down Expand Up @@ -2014,6 +1991,22 @@ def calc_AFunc(self, MaggNow, AaggNow):
return AggShocksDynamicRule(AFunc)


# Make a dictionary to specify a small open economy
init_small_open_economy = {
"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
"Rfree": 1.02, # exogenous and fixed return factor on assets
"wRte": 1.0, # exogenous and fixed wage rate
"PermGroFacAgg": 1.0, # Aggregate permanent income growth factor
"intercept_prev": 0.0, # Intercept of aggregate savings function
"slope_prev": 1.0, # Slope of aggregate savings function,
"verbose": False, # Whether to print solution progress to screen while solving
"max_loops": 1, # Maximum number of AFunc updating loops to allow, should always be 1
}


class SmallOpenEconomy(Market):
"""
A class for representing a small open economy, where the wage rate and interest rate are
Expand All @@ -2034,6 +2027,8 @@ class SmallOpenEconomy(Market):

def __init__(self, agents=None, tolerance=0.0001, act_T=1000, **kwds):
agents = agents if agents is not None else list()
params = init_small_open_economy.copy()
params.update(**kwds)
Market.__init__(
self,
agents=agents,
Expand All @@ -2052,6 +2047,7 @@ def __init__(self, agents=None, tolerance=0.0001, act_T=1000, **kwds):
distributions=["PermShkAggDstn", "TranShkAggDstn", "AggShkDstn"],
tolerance=tolerance,
act_T=act_T,
**params,
)
self.assign_parameters(**kwds)
self.update()
Expand Down Expand Up @@ -2230,8 +2226,8 @@ def get_AggShocks(self):
init_mrkv_cobb_douglas["PermGroFacAgg"] = [0.98, 1.02]
init_mrkv_cobb_douglas["MrkvArray"] = MrkvArray
init_mrkv_cobb_douglas["MrkvInit"] = 0
init_mrkv_cobb_douglas["slope_prev"] = 2 * [slope_prev]
init_mrkv_cobb_douglas["intercept_prev"] = 2 * [intercept_prev]
init_mrkv_cobb_douglas["slope_prev"] = 2 * [1.0]
init_mrkv_cobb_douglas["intercept_prev"] = 2 * [0.0]


class CobbDouglasMarkovEconomy(CobbDouglasEconomy):
Expand Down Expand Up @@ -2623,6 +2619,19 @@ def calc_AFunc(self, MaggNow, AaggNow):
return AggShocksDynamicRule(AFunc_list)


# Make a dictionary to specify a small open Markov economy
init_mrkv_small_open_economy = init_small_open_economy.copy()
init_mrkv_small_open_economy["PermShkAggStd"] = [0.012, 0.006]
init_mrkv_small_open_economy["TranShkAggStd"] = [0.006, 0.003]
init_mrkv_small_open_economy["PermGroFacAgg"] = [0.98, 1.02]
init_mrkv_small_open_economy["MrkvArray"] = MrkvArray
init_mrkv_small_open_economy["MrkvInit"] = 0
init_mrkv_small_open_economy["slope_prev"] = 2 * [1.0]
init_mrkv_small_open_economy["intercept_prev"] = 2 * [0.0]
init_mrkv_small_open_economy["Rfree"] = 1.02
init_mrkv_small_open_economy["wRte"] = 1.0


class SmallOpenMarkovEconomy(CobbDouglasMarkovEconomy, SmallOpenEconomy):
"""
A class for representing a small open economy, where the wage rate and interest rate are
Expand All @@ -2633,7 +2642,7 @@ class SmallOpenMarkovEconomy(CobbDouglasMarkovEconomy, SmallOpenEconomy):

def __init__(self, agents=None, tolerance=0.0001, act_T=1000, **kwds):
agents = agents if agents is not None else list()
temp_dict = init_mrkv_cobb_douglas.copy()
temp_dict = init_mrkv_small_open_economy.copy()
temp_dict.update(kwds)
CobbDouglasMarkovEconomy.__init__(
self,
Expand Down
2 changes: 1 addition & 1 deletion HARK/core.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -2324,7 +2324,7 @@ def solve_agent(agent, verbose, from_solution=None, from_t=None):
"Finished cycle #"
+ str(completed_cycles)
+ " in "
+ str(t_now - t_last)
+ "{:.6f}".format(t_now - t_last)
+ " seconds, solution distance = "
+ str(solution_distance)
)
Expand Down
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