sys.path.insert(0, os.path.abspath('../')) #Path to ConsumptionSaving folder
sys.path.insert(0, os.path.abspath('../../'))
sys.path.insert(0, os.path.abspath('../../cstwMPC')) #Path to cstwMPC folder
# Now, bring in what we need from the cstwMPC parameters
import SetupParamsCSTWnew as cstwParams
from HARKutilities import approxUniform
## Import the HARK ConsumerType we want
## Here, we bring in an agent making a consumption/savings decision every period, subject
## to transitory and permanent income shocks.
from ConsIndShockModel import IndShockConsumerType
# Now initialize a baseline consumer type, using default parameters from infinite horizon cstwMPC
BaselineType = IndShockConsumerType(**cstwParams.init_infinite)
BaselineType.AgentCount = 10000 # Assign the baseline consumer type to have many agents in simulation
####################################################################################################
####################################################################################################
"""
Now, add in ex-ante heterogeneity in consumers' discount factors
"""
# The cstwMPC parameters do not define a discount factor, since there is ex-ante heterogeneity
# in the discount factor. To prepare to create this ex-ante heterogeneity, first create
# the desired number of consumer types
from copy import deepcopy
num_consumer_types = 7 # declare the number of types we want
ConsumerTypes = [] # initialize an empty list
for nn in range(num_consumer_types):
import matplotlib.pyplot as plt
from copy import deepcopy
from time import clock
from HARK.parallel import multiThreadCommands, multiThreadCommandsFake
# Set up a baseline IndShockConsumerType, which will be replicated.
# In this case, we use a completely arbitrary 10 period lifecycle.
TestType = IndShockConsumerType(**Params.init_lifecycle)
TestType.TestVar = np.empty(
TestType.AgentCount
) # This is for making an empty buffer for diagnostics
TestType.CubicBool = True # Cubic interpolation must be on, because that's what's used in OpenCL version
T_sim = 10 # Choose simulation length
AgentCount = 100 # Chose number of agents in each type
TestType.T_sim = T_sim # Choose number of periods to simulate
TestType.AgentCount = AgentCount
TestType.initializeSim() # Set up some objects for simulation
# Make a list with many copies of the baseline agent type, each with a different CRRA.
TypeCount = 100
CRRAmin = 1.0
CRRAmax = 5.0
CRRAset = np.linspace(CRRAmin, CRRAmax, TypeCount)
TypeList = []
for j in range(TypeCount):
NewType = deepcopy(TestType)
NewType(CRRA=CRRAset[j])
TypeList.append(NewType)
# Construct an instance of IndShockConsumerTypesOpenCL by passing the type list to the constructor.
# Note the plural in the class name; each instance of this class represents *several* types.
