Merge pull request econ-ark#936 from sbenthall/i920

Remove "Now" from model variables

Documentation/CHANGELOG.md

@@ -28,6 +28,7 @@ interpolation for problems with CRRA utility. See [#888](https://github.com/econ
 * Fix the return fields of `dcegm/calcCrossPoints`[#909](https://github.com/econ-ark/HARK/pull/909).
 * Corrects location of constructor documentation to class string for Sphinx rendering [#908](https://github.com/econ-ark/HARK/pull/908)
 * Adds a module for producing life-cycle profiles of income shock variances from [Sabelhaus and Song (2010)](https://www.sciencedirect.com/science/article/abs/pii/S0304393210000358). See [#921](https://github.com/econ-ark/HARK/pull/921).
+* remove "Now" from model variable names [#936](https://github.com/econ-ark/HARK/pull/936)
 * Moves state MrkvNow to shocks['Mrkv'] in AggShockMarkov and KrusellSmith models [#935](https://github.com/econ-ark/HARK/pull/935)
 
 #### Minor Changes

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -169,7 +169,7 @@ def reset(self):
         """
         self.initializeSim()
         self.state_now['aLvlNow'] = self.kInit * np.ones(self.AgentCount)  # Start simulation near SS
-        self.state_now['aNrmNow'] = self.state_now['aLvlNow'] / self.state_now['pLvlNow'] # ???
+        self.state_now['aNrm'] = self.state_now['aLvlNow'] / self.state_now['pLvl'] # ???
 
     def preSolve(self):
         #        AgentType.preSolve()
@@ -283,12 +283,12 @@ def simBirth(self, which_agents):
         None
         """
         IndShockConsumerType.simBirth(self, which_agents)
-        if "aLvlNow" in self.state_now and self.state_now["aLvlNow"] is not None:
-            self.state_now["aLvlNow"][which_agents] = (
-                self.state_now["aNrmNow"][which_agents] * self.state_now["pLvlNow"][which_agents]
+        if 'aLvl' in self.state_now and self.state_now['aLvl'] is not None:
+            self.state_now['aLvl'][which_agents] = (
+                self.state_now['aNrm'][which_agents] * self.state_now['pLvl'][which_agents]
             )
         else:
-            self.state_now["aLvlNow"] = self.state_now['aNrmNow'] * self.state_now['pLvlNow']
+            self.state_now['aLvl'] = self.state_now['aNrm'] * self.state_now['pLvl']
 
     def simDeath(self):
         """
@@ -323,11 +323,11 @@ def simDeath(self):
 
         # Divide up the wealth of those who die, giving it to those who survive
         who_lives = np.logical_not(who_dies)
-        wealth_living = np.sum(self.state_now["aLvlNow"][who_lives])
-        wealth_dead = np.sum(self.state_now["aLvlNow"][who_dies])
+        wealth_living = np.sum(self.state_now['aLvl'][who_lives])
+        wealth_dead = np.sum(self.state_now['aLvl'][who_dies])
         Ractuarial = 1.0 + wealth_dead / wealth_living
-        self.state_now['aNrmNow'][who_lives] = self.state_now['aNrmNow'][who_lives] * Ractuarial
-        self.state_now["aLvlNow"][who_lives] = self.state_now["aLvlNow"][who_lives] * Ractuarial
+        self.state_now['aNrm'][who_lives] = self.state_now['aNrm'][who_lives] * Ractuarial
+        self.state_now['aLvl'][who_lives] = self.state_now['aLvl'][who_lives] * Ractuarial
         return who_dies
 
     def getRfree(self):
@@ -360,10 +360,10 @@ def getShocks(self):
         None
         """
         IndShockConsumerType.getShocks(self)  # Update idiosyncratic shocks
-        self.shocks["TranShkNow"] = (
-            self.shocks["TranShkNow"] * self.TranShkAggNow * self.wRteNow
+        self.shocks['TranShk'] = (
+            self.shocks['TranShk'] * self.TranShkAggNow * self.wRteNow
         )
-        self.shocks["PermShkNow"] = self.shocks["PermShkNow"] * self.PermShkAggNow
+        self.shocks['PermShk'] = self.shocks['PermShk'] * self.PermShkAggNow
 
     def getControls(self):
         """
@@ -382,12 +382,12 @@ def getControls(self):
         MaggNow = self.getMaggNow()
         for t in range(self.T_cycle):
             these = t == self.t_cycle
-            cNrmNow[these] = self.solution[t].cFunc(self.state_now["mNrmNow"][these], MaggNow[these])
+            cNrmNow[these] = self.solution[t].cFunc(self.state_now['mNrm'][these], MaggNow[these])
             MPCnow[these] = self.solution[t].cFunc.derivativeX(
-                self.state_now["mNrmNow"][these], MaggNow[these]
+                self.state_now['mNrm'][these], MaggNow[these]
             )  # Marginal propensity to consume
 
-        self.controls["cNrmNow"] = cNrmNow
+        self.controls['cNrm'] = cNrmNow
         self.MPCnow = MPCnow
         return None
 
@@ -599,8 +599,8 @@ def getShocks(self):
         # Store the shocks in self
         self.EmpNow = np.ones(self.AgentCount, dtype=bool)
         self.EmpNow[TranShkNow == self.IncUnemp] = False
-        self.shocks["TranShkNow"] = TranShkNow * self.TranShkAggNow * self.wRteNow
-        self.shocks["PermShkNow"] = PermShkNow * self.PermShkAggNow
+        self.shocks['TranShk'] = TranShkNow * self.TranShkAggNow * self.wRteNow
+        self.shocks['PermShk'] = PermShkNow * self.PermShkAggNow
 
     def getControls(self):
         """
@@ -632,15 +632,15 @@ def getControls(self):
             for i in range(StateCount):
                 those = np.logical_and(these, MrkvBoolArray[i, :])
                 cNrmNow[those] = self.solution[t].cFunc[i](
-                    self.state_now["mNrmNow"][those], MaggNow[those]
+                    self.state_now['mNrm'][those], MaggNow[those]
                 )
                 # Marginal propensity to consume
                 MPCnow[those] = (
                     self.solution[t]
                     .cFunc[i]
-                    .derivativeX(self.state_now["mNrmNow"][those], MaggNow[those])
+                    .derivativeX(self.state_now['mNrm'][those], MaggNow[those])
                 )
-        self.controls["cNrmNow"] = cNrmNow
+        self.controls['cNrm'] = cNrmNow
         self.MPCnow = MPCnow
         return None
 
@@ -1875,7 +1875,7 @@ def __init__(self, agents=None, tolerance=0.0001, act_T=1200, **kwds):
         Market.__init__(
             self,
             agents=agents,
-            reap_vars=["aLvlNow", "pLvlNow"],
+            reap_vars=['aLvl', 'pLvl'],
             track_vars=["MaggNow", "AaggNow"],
             dyn_vars=["AFunc"],
             tolerance=tolerance,
@@ -1896,14 +1896,14 @@ def __init__(self, agents=None, tolerance=0.0001, act_T=1200, **kwds):
         if not hasattr(self, "verbose"):
             self.verbose = True
 
-    def millRule(self, aLvlNow, pLvlNow):
+    def millRule(self, aLvl, pLvl):
         """
         Function to calculate the capital to labor ratio, interest factor, and
         wage rate based on each agent's current state.  Just calls calcRandW().
 
         See documentation for calcRandW for more information.
         """
-        return self.calcRandW(aLvlNow, pLvlNow)
+        return self.calcRandW(aLvl, pLvl)
 
     def calcDynamics(self, MaggNow, AaggNow):
         """
@@ -2651,14 +2651,19 @@ def makeMrkvHist(self):
         self.MrkvNow_hist = MrkvNow_hist
         self.act_T = act_T
 
-    def millRule(self, aLvlNow, pLvlNow):
+    def millRule(self, aLvl, pLvl):
         """
         Function to calculate the capital to labor ratio, interest factor, and
         wage rate based on each agent's current state.  Just calls calcRandW()
         and adds the Markov state index.
 
         See documentation for calcRandW for more information.
 
+        Params
+        -------
+        aLvl : float
+        pLvl : float
+
         Returns
         -------
         Mnow : float
@@ -2675,7 +2680,7 @@ def millRule(self, aLvlNow, pLvlNow):
             Binary indicator for bad (0) or good (1) macroeconomic state.
         """
         MrkvNow = self.MrkvNow_hist[self.Shk_idx]
-        temp = self.calcRandW(aLvlNow, pLvlNow)
+        temp = self.calcRandW(aLvl, pLvl)
 
         return temp + (MrkvNow,)
 

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -904,7 +904,7 @@ class GenIncProcessConsumerType(IndShockConsumerType):
         cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0
     )
 
-    state_vars = ["pLvlNow","mLvlNow","aLvlNow"]
+    state_vars = ['pLvl',"mLvl",'aLvl']
 
     def __init__(self, cycles=0, **kwds):
         params = init_explicit_perm_inc.copy()
@@ -915,12 +915,12 @@ def __init__(self, cycles=0, **kwds):
         self.solveOnePeriod = makeOnePeriodOOSolver(ConsGenIncProcessSolver)
 
         # a poststate?
-        self.state_now["aLvlNow"] = None
-        self.state_prev["aLvlNow"] = None
+        self.state_now['aLvl'] = None
+        self.state_prev['aLvl'] = None
 
         # better way to do this...
-        self.state_now["mLvlNow"] = None
-        self.state_prev["mLvlNow"] = None
+        self.state_now["mLvl"] = None
+        self.state_prev["mLvl"] = None
 
     def preSolve(self):
         #        AgentType.preSolve()
@@ -1100,10 +1100,10 @@ def simBirth(self, which_agents):
         aNrmNow_new = Lognormal(
             self.aNrmInitMean, self.aNrmInitStd, seed=self.RNG.randint(0, 2 ** 31 - 1)
         ).draw(N)
-        self.state_now['pLvlNow'][which_agents] = Lognormal(
+        self.state_now['pLvl'][which_agents] = Lognormal(
             self.pLvlInitMean, self.pLvlInitStd, seed=self.RNG.randint(0, 2 ** 31 - 1)
         ).draw(N)
-        self.state_now['aLvlNow'][which_agents] = aNrmNow_new * self.state_now['pLvlNow'][which_agents]
+        self.state_now['aLvl'][which_agents] = aNrmNow_new * self.state_now['pLvl'][which_agents]
         self.t_age[which_agents] = 0  # How many periods since each agent was born
         self.t_cycle[
             which_agents
@@ -1124,7 +1124,7 @@ def transition(self):
         pLvlNow
         mLvlNow
         """
-        aLvlPrev = self.state_prev['aLvlNow']
+        aLvlPrev = self.state_prev['aLvl']
         RfreeNow = self.getRfree()
 
         # Calculate new states: normalized market resources
@@ -1134,16 +1134,16 @@ def transition(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             pLvlNow[these] = (
-                self.pLvlNextFunc[t - 1](self.state_prev['pLvlNow'][these])
-                * self.shocks["PermShkNow"][these]
+                self.pLvlNextFunc[t - 1](self.state_prev['pLvl'][these])
+                * self.shocks['PermShk'][these]
             )
 
         #state value
         bLvlNow = RfreeNow * aLvlPrev  # Bank balances before labor income
 
         # Market resources after income - state value
         mLvlNow = bLvlNow + \
-                  self.shocks["TranShkNow"] * \
+                  self.shocks['TranShk'] * \
                   pLvlNow
 
         return (pLvlNow,
@@ -1168,12 +1168,12 @@ def getControls(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             cLvlNow[these] = self.solution[t].cFunc(
-                self.state_now["mLvlNow"][these], self.state_now["pLvlNow"][these]
+                self.state_now["mLvl"][these], self.state_now['pLvl'][these]
             )
             MPCnow[these] = self.solution[t].cFunc.derivativeX(
-                self.state_now["mLvlNow"][these], self.state_now["pLvlNow"][these]
+                self.state_now["mLvl"][these], self.state_now['pLvl'][these]
             )
-        self.controls["cLvlNow"] = cLvlNow
+        self.controls["cLvl"] = cLvlNow
         self.MPCnow = MPCnow
 
     def getPostStates(self):
@@ -1189,7 +1189,7 @@ def getPostStates(self):
         -------
         None
         """
-        self.state_now['aLvlNow'] = self.state_now["mLvlNow"] - self.controls["cLvlNow"]
+        self.state_now['aLvl'] = self.state_now["mLvl"] - self.controls["cLvl"]
         # moves now to prev
         AgentType.getPostStates(self)
 

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -1392,7 +1392,7 @@ class PerfForesightConsumerType(AgentType):
     )
     time_vary_ = ["LivPrb", "PermGroFac"]
     time_inv_ = ["CRRA", "Rfree", "DiscFac", "MaxKinks", "BoroCnstArt"]
-    state_vars = ['pLvlNow', 'PlvlAggNow', 'bNrmNow', 'mNrmNow', "aNrmNow"]
+    state_vars = ['pLvl', 'PlvlAgg', 'bNrm', 'mNrm', "aNrm"]
     shock_vars_ = []
 
     def __init__(self, cycles=1, verbose=1, quiet=False, **kwds):
@@ -1487,7 +1487,7 @@ def unpackcFunc(self):
 
     def initializeSim(self):
         self.PermShkAggNow = self.PermGroFacAgg  # This never changes during simulation
-        self.state_now['PlvlAggNow'] = 1.0
+        self.state_now['PlvlAgg'] = 1.0
         AgentType.initializeSim(self)
 
     def simBirth(self, which_agents):
@@ -1507,16 +1507,16 @@ def simBirth(self, which_agents):
         """
         # Get and store states for newly born agents
         N = np.sum(which_agents)  # Number of new consumers to make
-        self.state_now["aNrmNow"][which_agents] = Lognormal(
+        self.state_now['aNrm'][which_agents] = Lognormal(
             mu=self.aNrmInitMean,
             sigma=self.aNrmInitStd,
             seed=self.RNG.randint(0, 2 ** 31 - 1),
         ).draw(N)
         # why is a now variable set here? Because it's an aggregate.
         pLvlInitMeanNow = self.pLvlInitMean + np.log(
-            self.state_now["PlvlAggNow"]
+            self.state_now['PlvlAgg']
         )  # Account for newer cohorts having higher permanent income
-        self.state_now["pLvlNow"][which_agents] = Lognormal(
+        self.state_now['pLvl'][which_agents] = Lognormal(
             pLvlInitMeanNow,
             self.pLvlInitStd,
             seed=self.RNG.randint(0, 2 ** 31 - 1)
@@ -1570,10 +1570,10 @@ def getShocks(self):
         None
         """
         PermGroFac = np.array(self.PermGroFac)
-        self.shocks["PermShkNow"] = PermGroFac[
+        self.shocks['PermShk'] = PermGroFac[
             self.t_cycle - 1
         ]  # cycle time has already been advanced
-        self.shocks["TranShkNow"] = np.ones(self.AgentCount)
+        self.shocks['TranShk'] = np.ones(self.AgentCount)
 
     def getRfree(self):
         """
@@ -1592,16 +1592,16 @@ def getRfree(self):
         return RfreeNow
 
     def transition(self):
-        pLvlPrev = self.state_prev['pLvlNow']
-        aNrmPrev = self.state_prev['aNrmNow']
+        pLvlPrev = self.state_prev['pLvl']
+        aNrmPrev = self.state_prev['aNrm']
         RfreeNow = self.getRfree()
 
         # Calculate new states: normalized market resources and permanent income level
-        pLvlNow = pLvlPrev*self.shocks['PermShkNow'] # Updated permanent income level
-        PlvlAggNow = self.state_prev['PlvlAggNow']*self.PermShkAggNow # Updated aggregate permanent productivity level
-        ReffNow      = RfreeNow/self.shocks['PermShkNow'] # "Effective" interest factor on normalized assets
+        pLvlNow = pLvlPrev*self.shocks['PermShk'] # Updated permanent income level
+        PlvlAggNow = self.state_prev['PlvlAgg']*self.PermShkAggNow # Updated aggregate permanent productivity level
+        ReffNow      = RfreeNow/self.shocks['PermShk'] # "Effective" interest factor on normalized assets
         bNrmNow = ReffNow*aNrmPrev         # Bank balances before labor income
-        mNrmNow = bNrmNow + self.shocks['TranShkNow'] # Market resources after income
+        mNrmNow = bNrmNow + self.shocks['TranShk'] # Market resources after income
 
         return pLvlNow, PlvlAggNow, bNrmNow, mNrmNow, None
 
@@ -1623,9 +1623,9 @@ def getControls(self):
         for t in range(self.T_cycle):
             these = t == self.t_cycle
             cNrmNow[these], MPCnow[these] = self.solution[t].cFunc.eval_with_derivative(
-                self.state_now['mNrmNow'][these]
+                self.state_now['mNrm'][these]
             )
-        self.controls['cNrmNow'] = cNrmNow
+        self.controls['cNrm'] = cNrmNow
 
         # MPCnow is not really a control
         self.MPCnow = MPCnow
@@ -1644,9 +1644,9 @@ def getPostStates(self):
         None
         """
         # should this be "Now", or "Prev"?!?
-        self.state_now['aNrmNow'] = self.state_now['mNrmNow'] - self.controls['cNrmNow']
+        self.state_now['aNrm'] = self.state_now['mNrm'] - self.controls['cNrm']
         # Useful in some cases to precalculate asset level
-        self.state_now['aLvlNow'] = self.state_now['aNrmNow'] * self.state_now['pLvlNow']
+        self.state_now['aLvl'] = self.state_now['aNrm'] * self.state_now['pLvl']
 
         # moves now to prev
         super().getPostStates()
@@ -1865,7 +1865,7 @@ class IndShockConsumerType(PerfForesightConsumerType):
     time_inv_.remove(
         "MaxKinks"
     )  # This is in the PerfForesight model but not ConsIndShock
-    shock_vars_ = ["PermShkNow", "TranShkNow"]
+    shock_vars_ = ['PermShk', 'TranShk']
 
     def __init__(self, cycles=1, verbose=1, quiet=False, **kwds):
         params = init_idiosyncratic_shocks.copy()
@@ -2014,8 +2014,8 @@ def getShocks(self):
         # Store the shocks in self
         self.EmpNow = np.ones(self.AgentCount, dtype=bool)
         self.EmpNow[TranShkNow == self.IncUnemp] = False
-        self.shocks["PermShkNow"] = PermShkNow
-        self.shocks["TranShkNow"] = TranShkNow
+        self.shocks['PermShk'] = PermShkNow
+        self.shocks['TranShk'] = TranShkNow
 
     def calcBoundingValues(self):
         """
@@ -2701,7 +2701,7 @@ def getRfree(self):
              Array of size self.AgentCount with risk free interest rate for each agent.
         """
         RfreeNow = self.Rboro * np.ones(self.AgentCount)
-        RfreeNow[self.state_prev['aNrmNow'] > 0] = self.Rsave
+        RfreeNow[self.state_prev['aNrm'] > 0] = self.Rsave
         return RfreeNow
 
     def checkConditions(self):