Merge pull request #570 from sbenthall/i569-timing

Removing time flipping and time flow state
econ-ark · Apr 23, 2020 · 78d1981 · 78d1981
2 parents bc872eb + 4f325e2
commit 78d1981
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Showing 19 changed files with 808 additions and 287 deletions.
diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py
@@ -89,7 +89,7 @@ class AggShockConsumerType(IndShockConsumerType):
     evolves over time and take aggregate shocks into account when making their
     decision about how much to consume.
     '''
-    def __init__(self, time_flow=True, **kwds):
+    def __init__(self, **kwds):
         '''
         Make a new instance of AggShockConsumerType, an extension of
         IndShockConsumerType.  Sets appropriate solver and input lists.
@@ -98,7 +98,7 @@ def __init__(self, time_flow=True, **kwds):
         params.update(kwds)
 
         AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_),
-                           time_flow=time_flow, pseudo_terminal=False, **params)
+                           pseudo_terminal=False, **params)
 
         # Add consumer-type specific objects, copying to create independent versions
         self.time_vary = deepcopy(IndShockConsumerType.time_vary_)

diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py
@@ -985,7 +985,7 @@ class GenIncProcessConsumerType(IndShockConsumerType):
     solution_terminal_ = ConsumerSolution(cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0)
     poststate_vars_ = ['aLvlNow', 'pLvlNow']
 
-    def __init__(self, cycles=0, time_flow=True, **kwds):
+    def __init__(self, cycles=0, **kwds):
         '''
         Instantiate a new ConsumerType with given data.
         See ConsumerParameters.init_explicit_perm_inc for a dictionary of the
@@ -995,8 +995,6 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
         ----------
         cycles : int
             Number of times the sequence of periods should be solved.
-        time_flow : boolean
-            Whether time is currently "flowing" forward for this instance.
 
         Returns
         -------
@@ -1006,7 +1004,7 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
         params.update(kwds)
 
         # Initialize a basic ConsumerType
-        IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **params)
+        IndShockConsumerType.__init__(self, cycles=cycles, **params)
         self.solveOnePeriod = solveConsGenIncProcess  # idiosyncratic shocks solver with explicit persistent income
 
     def preSolve(self):
@@ -1109,8 +1107,6 @@ def updatepLvlGrid(self):
         -------
         None
         '''
-        orig_time = self.time_flow
-        self.timeFwd()
         LivPrbAll = np.array(self.LivPrb)
 
         # Simulate the distribution of persistent income levels by t_cycle in a lifecycle model
@@ -1163,8 +1159,6 @@ def updatepLvlGrid(self):
         # Store the result and add attribute to time_vary
         self.pLvlGrid = pLvlGrid
         self.addToTimeVary('pLvlGrid')
-        if not orig_time:
-            self.timeRev()
 
     def simBirth(self, which_agents):
         '''
@@ -1283,18 +1277,12 @@ def updatepLvlNextFunc(self):
         -------
         None
         '''
-        orig_time = self.time_flow
-        self.timeFwd()
-
         pLvlNextFunc = []
         for t in range(self.T_cycle):
             pLvlNextFunc.append(LinearInterp(np.array([0., 1.]), np.array([0., self.PermGroFac[t]])))
 
         self.pLvlNextFunc = pLvlNextFunc
         self.addToTimeVary('pLvlNextFunc')
-        if not orig_time:
-            self.timeRev()
-
 
 ###############################################################################
 
@@ -1335,7 +1323,6 @@ def __init__(self, cycles=0, time_flow=True, **kwds):
 
         GenIncProcessConsumerType.__init__(self,
                          cycles=cycles,
-                         time_flow=time_flow,
                          **params)
 
     def updatepLvlNextFunc(self):
@@ -1353,8 +1340,6 @@ def updatepLvlNextFunc(self):
         -------
         None
         '''
-        orig_time = self.time_flow
-        self.timeFwd()
 
         pLvlNextFunc = []
         pLogMean = self.pLvlInitMean  # Initial mean (log) persistent income
@@ -1365,5 +1350,3 @@ def updatepLvlNextFunc(self):
 
         self.pLvlNextFunc = pLvlNextFunc
         self.addToTimeVary('pLvlNextFunc')
-        if not orig_time:
-            self.timeRev()
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -1621,7 +1621,6 @@ class PerfForesightConsumerType(AgentType):
 
     def __init__(self,
                  cycles=1,
-                 time_flow=True,
                  verbose=False,
                  quiet=False,
                  **kwds):
@@ -1634,8 +1633,6 @@ def __init__(self,
         ----------
         cycles : int
             Number of times the sequence of periods should be solved.
-        time_flow : boolean
-            Whether time is currently "flowing" forward for this instance.
 
         Returns
         -------
@@ -1648,7 +1645,8 @@ def __init__(self,
 
         # Initialize a basic AgentType
         AgentType.__init__(self,solution_terminal=deepcopy(self.solution_terminal_),
-                           cycles=cycles,time_flow=time_flow,pseudo_terminal=False,**kwds)
+                           cycles=cycles,
+                           pseudo_terminal=False,**kwds)
 
         # Add consumer-type specific objects, copying to create independent versions
         self.time_vary      = deepcopy(self.time_vary_)
@@ -2047,7 +2045,6 @@ class IndShockConsumerType(PerfForesightConsumerType):
 
     def __init__(self,
                  cycles=1,
-                 time_flow=True,
                  verbose=False,
                  quiet=False,
                  **kwds):
@@ -2074,7 +2071,6 @@ def __init__(self,
         # Initialize a basic AgentType
         PerfForesightConsumerType.__init__(self,
                                            cycles=cycles,
-                                           time_flow=time_flow,
                                            verbose=verbose,
                                            quiet=quiet,
                                            **params)
@@ -2096,15 +2092,11 @@ def updateIncomeProcess(self):
         -----------
         none
         '''
-        original_time = self.time_flow
-        self.timeFwd()
         IncomeDstn, PermShkDstn, TranShkDstn = constructLognormalIncomeProcessUnemployment(self)
         self.IncomeDstn = IncomeDstn
         self.PermShkDstn = PermShkDstn
         self.TranShkDstn = TranShkDstn
         self.addToTimeVary('IncomeDstn','PermShkDstn','TranShkDstn')
-        if not original_time:
-            self.timeRev()
 
     def updateAssetsGrid(self):
         '''
@@ -2560,7 +2552,7 @@ class KinkedRconsumerType(IndShockConsumerType):
     time_inv_.remove('Rfree')
     time_inv_ += ['Rboro', 'Rsave']
 
-    def __init__(self,cycles=1,time_flow=True,**kwds):
+    def __init__(self,cycles=1,**kwds):
         '''
         Instantiate a new ConsumerType with given data.
         See ConsumerParameters.init_kinked_R for a dictionary of
@@ -2570,8 +2562,6 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         ----------
         cycles : int
             Number of times the sequence of periods should be solved.
-        time_flow : boolean
-            Whether time is currently "flowing" forward for this instance.
 
         Returns
         -------
@@ -2581,7 +2571,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         params.update(kwds)
 
         # Initialize a basic AgentType
-        PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**params)
+        PerfForesightConsumerType.__init__(self,
+                                           cycles=cycles,
+                                           **params)
 
         # Add consumer-type specific objects, copying to create independent versions
         self.solveOnePeriod = solveConsKinkedR # kinked R solver
@@ -2903,6 +2895,7 @@ def constructAssetsGrid(parameters):
 
     return aXtraGrid
 
+
 # Make a dictionary to specify a lifecycle consumer with a finite horizon
 init_lifecycle = copy(init_idiosyncratic_shocks)
 init_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02]
@@ -2920,4 +2913,3 @@ def constructAssetsGrid(parameters):
 init_cyclical['TranShkStd'] = [0.1,0.1,0.1,0.1]
 init_cyclical['LivPrb']     = 4*[0.98]
 init_cyclical['T_cycle']    = 4
-
diff --git a/HARK/ConsumptionSaving/ConsLaborModel.py b/HARK/ConsumptionSaving/ConsLaborModel.py
@@ -245,7 +245,7 @@ def solveConsLaborIntMarg(solution_next,PermShkDstn,TranShkDstn,LivPrb,DiscFac,C
 
 class LaborIntMargConsumerType(IndShockConsumerType):
 
-    '''        
+    '''
     A class representing agents who make a decision each period about how much
     to consume vs save and how much labor to supply (as a fraction of their time).
     They get CRRA utility from a composite good x_t = c_t*z_t^alpha, and discount
@@ -255,7 +255,7 @@ class LaborIntMargConsumerType(IndShockConsumerType):
     time_vary_ += ['WageRte']
     time_inv_ = copy(IndShockConsumerType.time_inv_)
 
-    def __init__(self,cycles=1,time_flow=True,**kwds):
+    def __init__(self,cycles=1,**kwds):
         '''
         Instantiate a new consumer type with given data.
         See ConsumerParameters.init_labor_intensive for a dictionary of
@@ -265,8 +265,6 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         ----------
         cycles : int
             Number of times the sequence of periods should be solved.
-        time_flow : boolean
-            Whether time is currently "flowing" forward for this instance.
         
         Returns
         -------
@@ -275,7 +273,7 @@ def __init__(self,cycles=1,time_flow=True,**kwds):
         params = init_labor_intensive.copy()
         params.update(kwds)
 
-        IndShockConsumerType.__init__(self,cycles = cycles,time_flow=time_flow,**params)
+        IndShockConsumerType.__init__(self,cycles = cycles,**params)
         self.pseudo_terminal = False
         self.solveOnePeriod = solveConsLaborIntMarg
         self.update()
@@ -318,13 +316,8 @@ def updateLbrCost(self):
         for n in range(N):
             LbrCostBase += Coeffs[n]*age_vec**n
         LbrCost = np.exp(LbrCostBase)
-        time_orig = self.time_flow
-        self.timeFwd()
         self.LbrCost = LbrCost.tolist()
         self.addToTimeVary('LbrCost')
-        if not time_orig:
-            self.timeRev()
-
 
     def calcBoundingValues(self):      
         '''
@@ -456,18 +449,11 @@ def updateTranShkGrid(self):
         -------
         None
         '''
-        time_orig=self.time_flow
-        self.timeFwd()
-
         TranShkGrid = []   # Create an empty list for TranShkGrid that will be updated
         for t in range(self.T_cycle):
             TranShkGrid.append(self.TranShkDstn[t][1])  # Update/ Extend the list of TranShkGrid with the TranShkVals for each TranShkPrbs
         self.TranShkGrid = TranShkGrid  # Save that list in self (time-varying)
         self.addToTimeVary('TranShkGrid')   # Run the method addToTimeVary from AgentType to add TranShkGrid as one parameter of time_vary list
-
-        if not time_orig:
-            self.timeRev()
-
 
     def updateSolutionTerminal(self):
         ''' 
@@ -481,11 +467,8 @@ def updateSolutionTerminal(self):
         Returns
         -------
         None
-        '''   
-        if self.time_flow: # To make sure we pick the last element of the list, depending on the direction time is flowing
-            t=-1
-        else:
-            t=0
+        '''
+        t=-1
         TranShkGrid = self.TranShkGrid[t]
         LbrCost = self.LbrCost[t]
         WageRte = self.WageRte[t]
@@ -613,28 +596,3 @@ def plotLbrFunc(self,t,bMin=None,bMax=None,ShkSet=None):
         plt.xlabel('Beginning of period bank balances')
         plt.ylabel('Labor supply')
         plt.show()
-
-
-# Make a default dictionary for the intensive margin labor supply model
-init_labor_intensive = copy(init_idiosyncratic_shocks)
-init_labor_intensive ['LbrCostCoeffs'] = [-1.0]
-init_labor_intensive ['WageRte'] = [1.0]
-init_labor_intensive['IncUnemp'] = 0.0
-init_labor_intensive['TranShkCount'] = 15 # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
-init_labor_intensive['PermShkCount'] = 16 # Crank up permanent shock count                  
-init_labor_intensive ['aXtraCount'] = 200 # May be important to have a larger number of gridpoints (than 48 initially)
-init_labor_intensive ['aXtraMax'] = 80.
-init_labor_intensive ['BoroCnstArt'] = None
-
-# Make a dictionary for intensive margin labor supply model with finite lifecycle
-init_labor_lifecycle = init_labor_intensive.copy()
-init_labor_lifecycle['PermGroFac'] = [1.01,1.01,1.01,1.01,1.01,1.02,1.02,1.02,1.02,1.02]
-init_labor_lifecycle['PermShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1]
-init_labor_lifecycle['TranShkStd'] = [0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1]
-init_labor_lifecycle['LivPrb']     = [0.99,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1] # Living probability decreases as time moves forward.
-init_labor_lifecycle['WageRte'] = [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0] # Wage rate in a lifecycle
-init_labor_lifecycle['LbrCostCoeffs'] = [-2.0, 0.4] # Assume labor cost coeffs is a polynomial of degree 1
-init_labor_lifecycle['T_cycle']    = 10
-#init_labor_lifecycle['T_retire']   = 7 # IndexError at line 774 in interpolation.py.
-init_labor_lifecycle['T_age']      = 11 # Make sure that old people die at terminal age and don't turn into newborns! 
-
diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py
@@ -683,8 +683,10 @@ class MarkovConsumerType(IndShockConsumerType):
     time_vary_ = IndShockConsumerType.time_vary_ + ['MrkvArray']
     shock_vars_ = IndShockConsumerType.shock_vars_ + ['MrkvNow']
 
-    def __init__(self,cycles=1,time_flow=True,**kwds):
-        IndShockConsumerType.__init__(self,cycles=1,time_flow=True,**kwds)
+    def __init__(self,
+                 cycles=1,
+                 **kwds):
+        IndShockConsumerType.__init__(self,cycles=1,**kwds)
         self.solveOnePeriod = _solveConsMarkov
         self.poststate_vars += ['MrkvNow']
         if not hasattr(self, 'global_markov'):

diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py
@@ -534,7 +534,7 @@ class MedShockConsumerType(PersistentShockConsumerType):
     '''
     shock_vars_ = PersistentShockConsumerType.shock_vars_ + ['MedShkNow']
 
-    def __init__(self,cycles=0,time_flow=True,**kwds):
+    def __init__(self,cycles=0,**kwds):
         '''
         Instantiate a new ConsumerType with given data, and construct objects
         to be used during solution (income distribution, assets grid, etc).
@@ -545,8 +545,6 @@ def __init__(self,cycles=0,time_flow=True,**kwds):
         ----------
         cycles : int
             Number of times the sequence of periods should be solved.
-        time_flow : boolean
-            Whether time is currently "flowing" forward for this instance.
 
         Returns
         -------
@@ -624,14 +622,9 @@ def updateSolutionTerminal(self):
         None
         '''
         # Take last period data, whichever way time is flowing
-        if self.time_flow:
-            MedPrice = self.MedPrice[-1]
-            MedShkVals = self.MedShkDstn[-1][1]
-            MedShkPrbs = self.MedShkDstn[-1][0]
-        else:
-            MedPrice = self.MedPrice[0]
-            MedShkVals = self.MedShkDstn[0][1]
-            MedShkPrbs = self.MedShkDstn[0][0]
+        MedPrice = self.MedPrice[-1]
+        MedShkVals = self.MedShkDstn[-1][1]
+        MedShkPrbs = self.MedShkDstn[-1][0]
 
         # Initialize grids of medical need shocks, market resources, and optimal consumption
         MedShkGrid = MedShkVals