def update_solution_terminal(self):
"""
Update the terminal period solution. This method should be run when a
new AgentType is created or when CRRA changes.
"""
self.solution_terminal_cs = ConsumerSolution(
cFunc=self.cFunc_terminal_,
vFunc=ValueFuncCRRA(self.cFunc_terminal_, self.CRRA),
vPfunc=MargValueFuncCRRA(self.cFunc_terminal_, self.CRRA),
vPPfunc=MargMargValueFuncCRRA(self.cFunc_terminal_, self.CRRA),
mNrmMin=0.0,
hNrm=0.0,
MPCmin=1.0,
MPCmax=1.0,
)
def add_vPPfunc(self, solution):
"""
Adds the marginal marginal value function to an existing solution, so
that the next solver can evaluate vPP and thus use cubic interpolation.
Parameters
----------
solution : ConsumerSolution
The solution to this single period problem, which must include the
consumption function.
Returns
-------
solution : ConsumerSolution
The same solution passed as input, but with the marginal marginal
value function for this period added as the attribute vPPfunc.
"""
vPPfuncNow = MargMargValueFuncCRRA(solution.cFunc, self.CRRA)
solution.vPPfunc = vPPfuncNow
return solution
def update_solution_terminal(self):
"""
Update the terminal period solution. This method should be run when a
new AgentType is created or when CRRA changes.
Parameters
----------
None
Returns
-------
None
"""
self.solution_terminal.vFunc = ValueFuncCRRA(self.cFunc_terminal_,
self.CRRA)
self.solution_terminal.vPfunc = MargValueFuncCRRA(
self.cFunc_terminal_, self.CRRA)
self.solution_terminal.vPPfunc = MargMargValueFuncCRRA(
self.cFunc_terminal_, self.CRRA)
self.solution_terminal.hNrm = 0.0 # Don't track normalized human wealth
self.solution_terminal.hLvl = lambda p: np.zeros_like(p)
# But do track absolute human wealth by persistent income
self.solution_terminal.mLvlMin = lambda p: np.zeros_like(p)