def updateRiskyDstn(self):
"""
Creates the attributes RiskyDstn from the primitive attributes RiskyAvg,
RiskyStd, and RiskyCount, approximating the (perceived) distribution of
returns in each period of the cycle.
Parameters
----------
None
Returns
-------
None
"""
# Determine whether this instance has time-varying risk perceptions
if (
(type(self.RiskyAvg) is list)
and (type(self.RiskyStd) is list)
and (len(self.RiskyAvg) == len(self.RiskyStd))
and (len(self.RiskyAvg) == self.T_cycle)
):
self.addToTimeVary("RiskyAvg", "RiskyStd")
elif (type(self.RiskyStd) is list) or (type(self.RiskyAvg) is list):
raise AttributeError(
"If RiskyAvg is time-varying, then RiskyStd must be as well, and they must both have length of T_cycle!"
)
else:
self.addToTimeInv("RiskyAvg", "RiskyStd")
# Generate a discrete approximation to the risky return distribution if the
# agent has age-varying beliefs about the risky asset
if "RiskyAvg" in self.time_vary:
self.RiskyDstn = []
for t in range(self.T_cycle):
self.RiskyDstn.append(
Lognormal.from_mean_std(
self.RiskyAvg[t],
self.RiskyStd[t]
).approx(self.RiskyCount)
)
self.addToTimeVary("RiskyDstn")
# Generate a discrete approximation to the risky return distribution if the
# agent does *not* have age-varying beliefs about the risky asset (base case)
else:
self.RiskyDstn = Lognormal.from_mean_std(
self.RiskyAvg,
self.RiskyStd,
).approx(self.RiskyCount)
self.addToTimeInv("RiskyDstn")
def get_Risky(self):
"""
Sets the attribute Risky as a single draw from a lognormal distribution.
Uses the attributes RiskyAvgTrue and RiskyStdTrue if RiskyAvg is time-varying,
else just uses the single values from RiskyAvg and RiskyStd.
Parameters
----------
None
Returns
-------
None
"""
if "RiskyDstn" in self.time_vary:
RiskyAvg = self.RiskyAvgTrue
RiskyStd = self.RiskyStdTrue
else:
RiskyAvg = self.RiskyAvg
RiskyStd = self.RiskyStd
self.shocks["Risky"] = Lognormal.from_mean_std(
RiskyAvg, RiskyStd, seed=self.RNG.randint(0, 2 ** 31 - 1)
).draw(1)
