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[WIP] cleaning up strange newborn handling in ConsIndShock model #1021

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[WIP] cleaning up strange newborn handling in ConsIndShock model #1021

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26 changes: 6 additions & 20 deletions HARK/ConsumptionSaving/ConsIndShockModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2171,9 +2171,12 @@ def get_shocks(self):
"""
PermShkNow = np.zeros(self.AgentCount) # Initialize shock arrays
TranShkNow = np.zeros(self.AgentCount)
newborn = self.t_age == 0
for t in range(self.T_cycle):
these = t == self.t_cycle

draw_age = self.t_cycle - 1
draw_age[draw_age < 0] = 0

for t in np.unique(draw_age):
these = t == draw_age

@Mv77 Mv77 Jun 23, 2021

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I'm now realizing that draw_age already contains the -1 index shift. Therefore, I think indices inside the cycle should be t, not t-1. Does that sound right?

@sbenthall sbenthall Jun 23, 2021

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Yes, thank you, that's right.

N = np.sum(these)
if N > 0:
IncShkDstnNow = self.IncShkDstn[
Expand All @@ -2188,23 +2191,6 @@ def get_shocks(self):
) # permanent "shock" includes expected growth
TranShkNow[these] = IncShks[1, :]

# That procedure used the *last* period in the sequence for newborns, but that's not right
# Redraw shocks for newborns, using the *first* period in the sequence. Approximation.
N = np.sum(newborn)
if N > 0:
these = newborn
IncShkDstnNow = self.IncShkDstn[0] # set current income distribution
PermGroFacNow = self.PermGroFac[0] # and permanent growth factor

# Get random draws of income shocks from the discrete distribution
EventDraws = IncShkDstnNow.draw_events(N)
PermShkNow[these] = (
IncShkDstnNow.X[0][EventDraws] * PermGroFacNow
) # permanent "shock" includes expected growth
TranShkNow[these] = IncShkDstnNow.X[1][EventDraws]
# PermShkNow[newborn] = 1.0
TranShkNow[newborn] = 1.0

@Mv77 Mv77 Jun 21, 2021

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This line might be one of the sources of failing tests.

@Mv77 Mv77 Jun 21, 2021

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2206 is the line I mean.


# Store the shocks in self
self.EmpNow = np.ones(self.AgentCount, dtype=bool)
self.EmpNow[TranShkNow == self.IncUnemp] = False
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