for j in range(ImmunityT):
MrkvArray[
j + 1, j
] = (
1.0
) # When immune, have 100% chance of transition to state with one fewer immunity periods remaining
# %%
init_unemployment_immunity = copy(init_idiosyncratic_shocks)
init_unemployment_immunity["MrkvArray"] = [MrkvArray]
ImmunityExample = MarkovConsumerType(**init_unemployment_immunity)
ImmunityExample.assignParameters(
Rfree=np.array(np.array(StateCount * [1.03])), # Interest factor same in all states
PermGroFac=[
np.array(StateCount * [1.01])
], # Permanent growth factor same in all states
LivPrb=[np.array(StateCount * [0.98])], # Same survival probability in all states
BoroCnstArt=None, # No artificial borrowing constraint
cycles=0,
) # Infinite horizon
ImmunityExample.IncomeDstn = [IncomeDstn]
# %%
# Solve the unemployment immunity problem and display the consumption functions
start_time = process_time()
ImmunityExample.solve()
end_time = process_time()
print(
'Solving an "unemployment immunity" consumer took '
+ mystr(end_time - start_time)
+ " seconds."
# Here, we bring in an agent making a consumption/savings decision every period, subject to transitory and permanent income shocks, AND a Markov shock
# %%
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# %% [markdown]
# Currently, Markov states can differ in their interest factor, permanent growth factor, survival probability, and income distribution. Each of these needs to be specifically set.
#
# Do that here, except shock distribution, which will be done later (because we want to examine the consequences of different shock distributions).
# %%
GrowthFastAnn = 1.06 # Six percent annual growth
GrowthSlowAnn = 1.00 # Stagnation
ChinaExample.assignParameters(PermGroFac = [np.array([GrowthSlowAnn,GrowthFastAnn ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #needs to be an array, of shape (StateCount,)
LivPrb = [np.array(StateCount*[init_China_parameters['LivPrb']][0])], #needs to be a list, with 0th element of shape of shape (StateCount,)
cycles = 0)
ChinaExample.track_vars = ['aNrmNow','cNrmNow','pLvlNow'] # Names of variables to be tracked
# %% [markdown]
# Now, add in ex-ante heterogeneity in consumers' discount factors.
#
# The cstwMPC parameters do not define a single discount factor; instead, 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:
#
# %%
num_consumer_types = 7 # declare the number of types we want
ChineseConsumerTypes = [] # initialize an empty list
for nn in range(num_consumer_types):
#
# Here, we bring in an agent making a consumption/savings decision every period, subject to transitory and permanent income shocks, AND a Markov shock
# %%
from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
ChinaExample = MarkovConsumerType(**init_China_parameters)
# %% [markdown]
# Currently, Markov states can differ in their interest factor, permanent growth factor, survival probability, and income distribution. Each of these needs to be specifically set.
#
# Do that here, except shock distribution, which will be done later (because we want to examine the consequences of different shock distributions).
# %%
GrowthFastAnn = 1.06 # Six percent annual growth
GrowthSlowAnn = 1.00 # Stagnation
ChinaExample.assignParameters(PermGroFac = [np.array([GrowthSlow.,GrowthFast ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,)
Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #needs to be an array, of shape (StateCount,)
LivPrb = [np.array(StateCount*[init_China_parameters['LivPrb']][0])], #needs to be a list, with 0th element of shape of shape (StateCount,)
cycles = 0)
ChinaExample.track_vars = ['aNrmNow','cNrmNow','pLvlNow'] # Names of variables to be tracked
# %% [markdown]
# Now, add in ex-ante heterogeneity in consumers' discount factors.
#
# The cstwMPC parameters do not define a single discount factor; instead, 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:
#
# %%
num_consumer_types = 7 # declare the number of types we want
ChineseConsumerTypes = [] # initialize an empty list