# survival probability, and income distribution. Each of these needs to be specifically set. # Do that here, except income distribution. That will be done later, because we want to examine # the effects of different income distributions. ChinaExample.assignParameters( PermGroFac=[ np.array([1., 1.06**(.25)]) ], #needs to be a list, with 0th element of shape of shape (StateCount,) Rfree=np.array(StateCount * [init_China_parameters['Rfree'] ]), #need 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 #################################################################################################### #################################################################################################### """ 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 num_consumer_types = 7 # declare the number of types we want ChineseConsumerTypes = [] # initialize an empty list for nn in range(num_consumer_types):
### Import and initialize 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, AND a Markov shock from ConsMarkovModel import MarkovConsumerType ChinaExample = MarkovConsumerType(**init_China_parameters) # 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 income distribution. That will be done later, because we want to examine # the effects of different income distributions. ChinaExample.assignParameters(PermGroFac = [np.array([1.,1.06 ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,) Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #need 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 #################################################################################################### #################################################################################################### """ 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 num_consumer_types = 7 # declare the number of types we want ChineseConsumerTypes = [] # initialize an empty list for nn in range(num_consumer_types):