def __init__(self,
cycles=1,
**kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
See ConsumerParameters.init_pref_shock for a dictionary of
the keywords that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
Returns
-------
None
'''
params = init_preference_shocks.copy()
params.update(kwds)
IndShockConsumerType.__init__(self,
cycles=cycles,
**params)
self.solveOnePeriod = makeOnePeriodOOSolver(ConsPrefShockSolver)
def __init__(self, **kwargs):
IndShockConsumerType.__init__(self, **kwargs)
# Add consumer-type specific objects, copying to create independent versions
if (not self.CubicBool) and (not self.vFuncBool):
solver = ConsIndShockSolverBasicFast
else: # Use the "advanced" solver if either is requested
solver = ConsIndShockSolverFast
self.solveOnePeriod = makeOnePeriodOOSolver(solver)
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, cycles=1, **kwds):
"""
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
See init_kinky_pref for a dictionary of the keywords
that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
Returns
-------
None
"""
params = init_kinky_pref.copy()
params.update(kwds)
kwds = params
IndShockConsumerType.__init__(self, **kwds)
self.solveOnePeriod = makeOnePeriodOOSolver(ConsKinkyPrefSolver)
self.addToTimeInv("Rboro", "Rsave")
self.delFromTimeInv("Rfree")
def __init__(self, **kwargs):
PerfForesightConsumerType.__init__(self, **kwargs)
self.solveOnePeriod = makeOnePeriodOOSolver(
ConsPerfForesightSolverFast)