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Merged
sbenthall merged 4 commits into from
Feb 16, 2023
Merged

update cycles = 0 #194

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19a79b6
update cycles = 0
alanlujan91 Jan 31, 2023
0785401
fix Rfree bug
alanlujan91 Feb 1, 2023
3e9859c
Merge remote-tracking branch 'upstream/master' into infinite_lucas
alanlujan91 Feb 1, 2023
349808b
conditional
alanlujan91 Feb 3, 2023
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22 changes: 5 additions & 17 deletions sharkfin/population.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@ class AgentPopulation:
dollars_per_hark_money_unit: float = 1500

def __post_init__(self):

self.time_var = self.agent_class.time_vary
self.time_inv = self.agent_class.time_inv

Expand All @@ -45,12 +44,10 @@ def __post_init__(self):
self.stored_class_stats = None

def infer_counts(self):

param_dict = self.parameter_dict

# if agent_clas_count is not specified, infer from parameters
if self.agent_class_count is None:

agent_class_count = 1
for key_param in param_dict:
parameter = param_dict[key_param]
Expand All @@ -63,7 +60,6 @@ def infer_counts(self):
self.agent_class_count = agent_class_count

if self.t_age is None:

t_age = 1
for key_param in param_dict:
parameter = param_dict[key_param]
Expand All @@ -79,7 +75,6 @@ def infer_counts(self):
# return t_age and agent_class_count

def approx_distributions(self, approx_params: dict):

param_dict = self.parameter_dict

self.continuous_distributions = {}
Expand Down Expand Up @@ -111,7 +106,6 @@ def approx_distributions(self, approx_params: dict):
self.infer_counts()

def parse_params(self):

param_dict = self.parameter_dict

agent_dicts = [] # container for dictionaries of each agent subgroup
Expand Down Expand Up @@ -227,7 +221,6 @@ def class_stats(self, store=False):
return cs

def create_distributed_agents(self):

rng = self.rng if self.rng is not None else np.random.default_rng()

self.agents = [
Expand All @@ -236,7 +229,6 @@ def create_distributed_agents(self):
]

def create_database(self):

database = pd.DataFrame(self.agent_dicts)
database["agents"] = self.agents

Expand All @@ -249,7 +241,6 @@ def solve_distributed_agents(self):
agent.solve()

def unpack_solutions(self):

self.solution = [agent.solution for agent in self.agents]

def init_simulation(self, T_sim=1000):
Expand All @@ -262,9 +253,12 @@ def init_simulation(self, T_sim=1000):
agent.initialize_sim()

if self.stored_class_stats is None:

## build an IndShockConsumerType "double" of this agent, with the same parameters
ind_shock_double = cism.IndShockConsumerType(**agent.parameters)
parameters = agent.parameters.copy()
if "Rfree" in parameters and isinstance(parameters["Rfree"], list):
# patch potential bug until HARK is updated
parameters["Rfree"] = parameters["Rfree"][0]
ind_shock_double = cism.IndShockConsumerType(**parameters)

## solve to get the mNrmStE value
## that is, the Steady-state Equilibrium value of mNrm, for the IndShockModel
Expand All @@ -290,7 +284,6 @@ def init_simulation(self, T_sim=1000):
agent.state_now["aLvl"] = agent.state_now["aNrm"] * agent.state_now["pLvl"]

def solve(self, merge_by=None):

self.solve_distributed_agents()

self.solution = AgentPopulationSolution(self)
Expand Down Expand Up @@ -533,17 +526,14 @@ def __init__(self, agent_population):
self.agent_database = self.agent_population.agent_database

def merge_solutions(self, continuous_states):

if continuous_states is None or continuous_states == []:

if self.dist_params is None or self.dist_params == []:
self.solution_database = self.agent_database
else:
self.solution_database = self.agent_database.set_index(self.dist_params)
self.ex_ante_hetero_params = []

else:

# check that continous states are in heterogeneous parameters
for state in continuous_states:
if state not in self.dist_params:
Expand All @@ -557,7 +547,6 @@ def merge_solutions(self, continuous_states):
self._merge_solutions_3d(continuous_states)

def _merge_solutions_2d(self, continuous_states):

discrete_params = list(set(self.dist_params) - set(continuous_states))
discrete_params.sort()

Expand Down Expand Up @@ -620,7 +609,6 @@ def _merge_solutions_2d(self, continuous_states):
self.solution_database = self.solution_database.set_index(discrete_params)

def _merge_solutions_3d(self, continuous_states):

discrete_params = list(set(self.dist_params) - set(continuous_states))
discrete_params.sort()

Expand Down
32 changes: 19 additions & 13 deletions simulate/parameters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,10 +6,13 @@

from sharkfin.population import AgentPopulation

def build_population(agent_type, parameters, rng = None, dphm = 1500):
pop = AgentPopulation(agent_type(), parameters, rng = rng, dollars_per_hark_money_unit = dphm)
if 'approx_params' in parameters:
pop.approx_distributions(parameters['approx_params'])

def build_population(agent_type, parameters, rng=None, dphm=1500):
pop = AgentPopulation(
agent_type(), parameters, rng=rng, dollars_per_hark_money_unit=dphm
)
if "approx_params" in parameters:
pop.approx_distributions(parameters["approx_params"])
pop.parse_params()

pop.create_distributed_agents()
Expand Down Expand Up @@ -60,9 +63,11 @@ def build_population(agent_type, parameters, rng = None, dphm = 1500):

### Configuring the agent population

whiteshark_parameter_dict = whiteshark_agent_population_params | whiteshark_continuous_dist_params
whiteshark_parameter_dict['approx_params'] = whiteshark_approx_params
whiteshark_parameter_dict['ex_post'] = ["RiskyAvg", "RiskyStd"]
whiteshark_parameter_dict = (
whiteshark_agent_population_params | whiteshark_continuous_dist_params
)
whiteshark_parameter_dict["approx_params"] = whiteshark_approx_params
whiteshark_parameter_dict["ex_post"] = ["RiskyAvg", "RiskyStd"]
whiteshark_parameter_dict["AgentCount"] = 1

WHITESHARK = whiteshark_parameter_dict
Expand All @@ -76,23 +81,24 @@ def build_population(agent_type, parameters, rng = None, dphm = 1500):
### TODO: Population generators that take parameters like CRRA, DisCFac

lucas0_agent_population_params = {
"cycles": 0, # issue 186
"aNrmInitStd": 0.0,
"LivPrb": 0.98**0.25,
"PermGroFac": 1.0,
"pLvlInitMean": 0.0, ## Intention: Shut down income. But this might not do it.
"pLvlInitMean": 0.0, ## Intention: Shut down income. But this might not do it.
"pLvlInitStd": 0.0,
"Rfree": 1.0,
# Scaling from annual to quarterly
"TranShkStd": [0],
"PermShkStd": [0],
### These are placeholders that will be set when the system is set up.
"CRRA" : 5,
"DiscFac" : 0.96,
"ex_post" : None # ex post heterogeneous parameters over which to merge solutions
"CRRA": 5,
"DiscFac": 0.96,
"ex_post": None, # ex post heterogeneous parameters over which to merge solutions
}

lucas0_parameter_dict = lucas0_agent_population_params
lucas0_parameter_dict["AgentCount"] = 10 # TODO: What should this be?
lucas0_parameter_dict["AgentCount"] = 10 # TODO: What should this be?


LUCAS0 = lucas0_parameter_dict
LUCAS0 = lucas0_parameter_dict