def __init__(self, cycles=0, **kwds):
'''
Instantiate a new TractableConsumerType with given data.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
Returns:
-----------
New instance of TractableConsumerType.
'''
# Initialize a basic AgentType
AgentType.__init__(self, cycles=cycles, pseudo_terminal=True, **kwds)
# Add consumer-type specific objects, copying to create independent versions
self.time_vary = []
self.time_inv = [
'DiscFac', 'Rfree', 'CRRA', 'PermGroFacCmp', 'UnempPrb', 'PFMPC',
'Rnrm', 'Beth', 'mLowerBnd', 'mUpperBnd'
]
self.shock_vars = ['eStateNow']
self.poststate_vars = ['aLvlNow', 'eStateNow'] # For simulation
self.solveOnePeriod = addToStableArmPoints # set correct solver
def __init__(self, **kwds):
'''
Instantiate a new FashionVictim with given data.
Parameters
----------
**kwds : keyword arguments
Any number of keyword arguments key=value; each value will be assigned
to the attribute key in the new instance.
Returns
-------
new instance of FashionVictimType
'''
# Initialize a basic AgentType
AgentType.__init__(
self,
solution_terminal=FashionVictimType._solution_terminal,
cycles=0,
time_flow=True,
pseudo_terminal=True,
**kwds)
# Add class-specific features
self.time_inv = [
'DiscFac', 'conformUtilityFunc', 'punk_utility', 'jock_utility',
'switchcost_J2P', 'switchcost_P2J', 'pGrid', 'pEvolution',
'pref_shock_mag'
]
self.time_vary = []
self.solveOnePeriod = solveFashion
self.update()
def __init__(self, time_flow=True, **kwds):
'''
Make a new consumer type for the cstwMPC model.
Parameters
----------
time_flow : boolean
Indictator for whether time is "flowing" forward for this agent.
**kwds : keyword arguments
Any number of keyword arguments of the form key=value. Each value
will be assigned to the attribute named in self.
Returns
-------
new instance of cstwMPCagent
'''
# Initialize a basic AgentType
AgentType.__init__(self,
solution_terminal=deepcopy(
Model.IndShockConsumerType.solution_terminal_),
time_flow=time_flow,
pseudo_terminal=False,
**kwds)
# Add consumer-type specific objects, copying to create independent versions
self.time_vary = deepcopy(Model.IndShockConsumerType.time_vary_)
self.time_inv = deepcopy(Model.IndShockConsumerType.time_inv_)
self.solveOnePeriod = Model.solveConsIndShock
self.update()
def __init__(self, cycles=0, **kwds):
"""
Instantiate a new TractableConsumerType with given data.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
Returns:
-----------
New instance of TractableConsumerType.
"""
# Initialize a basic AgentType
AgentType.__init__(self, cycles=cycles, pseudo_terminal=True, **kwds)
# Add consumer-type specific objects, copying to create independent versions
self.time_vary = []
self.time_inv = [
"DiscFac",
"Rfree",
"CRRA",
"PermGroFacCmp",
"UnempPrb",
"PFMPC",
"Rnrm",
"Beth",
"mLowerBnd",
"mUpperBnd",
]
self.shock_vars = ["eStateNow"]
self.poststate_vars = ["aLvlNow", "eStateNow"] # For simulation
self.solveOnePeriod = addToStableArmPoints # set correct solver
def pre_solve(self):
"""
Check to make sure that the inputs that are specific to MarkovConsumerType
are of the right shape (if arrays) or length (if lists).
Parameters
----------
None
Returns
-------
None
"""
AgentType.pre_solve(self)
self.check_markov_inputs()
def get_poststates(self):
"""
Calculates end-of-period assets for each consumer of this type.
Identical to version in IndShockConsumerType but uses Lvl rather than Nrm variables.
Parameters
----------
None
Returns
-------
None
"""
self.state_now['aLvl'] = self.state_now["mLvl"] - self.controls["cLvl"]
# moves now to prev
AgentType.get_poststates(self)
def __init__(self, cycles=0, verbose=1, quiet=False, **kwds):
self.time_vary = []
self.time_inv = [
"CRRA", "Rfree", "DiscFac", "phi", "eta", "nu", "pssi", "B"
]
self.state_vars = []
self.shock_vars = []
cmin = 1e-6 # lower bound on consumption
Matlabgrid = loadmat('Bgrid')
griddata = list(Matlabgrid.items())
datagrid_array = np.array(griddata)
print(datagrid_array[3, 1])
Bgrid_uc = datagrid_array[3, 1]
params = init_GL.copy()
#params.update(kwds)
kwds = params
print(kwds)
#setup grid based on constraint phi
Bgrid = []
for i in range(200):
if Bgrid_uc[0, i] > phi:
Bgrid.append(Bgrid_uc[0, i])
Bgrid = np.array(Bgrid).reshape(1, len(Bgrid))
#initial Guess for Cpolicy
Cguess = np.maximum(Rfree * np.ones(13).reshape(13, 1).dot(Bgrid),
cmin)
# Define some universal values for all consumer types
solution_terminal_ = GLConsumerSolution(Cpol=Cguess, )
AgentType.__init__(
self,
solution_terminal=deepcopy(solution_terminal_),
cycles=cycles,
pseudo_terminal=False,
**kwds) #going to need to specify **kwds and solution_terminal_
self.verbose = verbose
self.quiet = quiet
self.solveOnePeriod = makeOnePeriodOOSolver(GLsolver)
def __init__(self, **kwds):
params = init_tractable.copy()
params.update(kwds)
# Initialize a basic AgentType
AgentType.__init__(self, pseudo_terminal=True, **params)
# Add consumer-type specific objects, copying to create independent versions
self.time_vary = []
self.time_inv = [
"DiscFac",
"Rfree",
"CRRA",
"PermGroFacCmp",
"UnempPrb",
"PFMPC",
"Rnrm",
"Beth",
"mLowerBnd",
"mUpperBnd",
]
self.shock_vars = ["eStateNow"]
self.poststate_vars = ['aLvl', "eStateNow"] # For simulation
self.solve_one_period = add_to_stable_arm_points # set correct solver
def preSolve(self):
AgentType.preSolve(self)
self.updateSolutionTerminal()
def pre_solve(self):
AgentType.pre_solve(self)
self.update_solution_terminal()
