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):
# Now create the types, and append them to the list ChineseConsumerTypes
newType = deepcopy(ChinaExample)
ChineseConsumerTypes.append(newType)
"Solving a Markov consumer with serially correlated unemployment took "
+ mystr(end_time - start_time)
+ " seconds."
)
print("Consumption functions for each discrete state:")
plotFuncs(SerialUnemploymentExample.solution[0].cFunc, 0, 50)
if SerialUnemploymentExample.vFuncBool:
print("Value functions for each discrete state:")
plotFuncs(SerialUnemploymentExample.solution[0].vFunc, 5, 50)
# %%
# Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist
if do_simulation:
SerialUnemploymentExample.T_sim = 120
SerialUnemploymentExample.MrkvPrbsInit = [0.25, 0.25, 0.25, 0.25]
SerialUnemploymentExample.track_vars = ["mNrmNow", "cNrmNow"]
SerialUnemploymentExample.makeShockHistory() # This is optional
SerialUnemploymentExample.initializeSim()
SerialUnemploymentExample.simulate()
# %% [markdown]
# ### 2. Unemployment immunity for a fixed period
#
# Let's create a consumer similar to the one in "idiosyncratic shock" model but who occasionally gets "unemployment immunity" for a fixed period in an economy subject to boom and bust cycles.
# %%
# Make a consumer who occasionally gets "unemployment immunity" for a fixed period
UnempPrb = 0.05 # Probability of becoming unemployed each period
ImmunityPrb = 0.01 # Probability of becoming "immune" to unemployment
ImmunityT = 6 # Number of periods of immunity