Small changes

Search for mNrmTrg even if GICMod fails. Also put mNrmBal and mNrmTrg into top level. Need to change later. These changes were made while updating the BST dashboard notebook to be compatible.

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -443,9 +443,8 @@ class ConsIndShockSetup(ConsPerfForesightSolver):
     solution_next : ConsumerSolution
         The solution to next period's one period problem.
     IncShkDstn : distribution.Distribution
-        A discrete
-        approximation to the income process between the period being solved
-        and the one immediately following (in solution_next).
+        A discrete approximation to the income process between the period being
+        solved and the one immediately following (in solution_next).
     LivPrb : float
         Survival probability; likelihood of being alive at the beginning of
         the succeeding period.
@@ -671,7 +670,7 @@ def prepare_to_calc_EndOfPrdvP(self):
         # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid)
         # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption
         # function as the lower envelope of the (by the artificial borrowing con-
-        # straint) uconstrained consumption function, and the artificially con-
+        # straint) unconstrained consumption function, and the artificially con-
         # strained consumption function.
         self.aNrmNow = np.asarray(self.aXtraGrid) + self.BoroCnstNat
 
@@ -3243,7 +3242,7 @@ def check_conditions(self, verbose=None):
         if self.conditions['GICMod']:
             GICMod_message = '\nBecause the GICMod is satisfied, expected growth of the ratio of market resources to permanent income is less than one as market resources become arbitrarily large. Hence the consumer has a target ratio of market resources to permanent income.'
         else:
-            GICMod_message = '\nBecause the GICMod is violated, expected growth of the ratio of market resources to permanent income exceeds one for all levels of market resources. Hence the consumer does not have a target ratio of market resources to permanent income.'
+            GICMod_message = '\nBecause the GICMod is violated, expected growth of the ratio of market resources to permanent income exceeds one as market resources go to infinity. Hence the consumer might not have a target ratio of market resources to permanent income.'
         self.log_condition_result(None, None, GICMod_message, verbose)
 
         # Report on whether a target level of wealth exists at the aggregate level
@@ -3272,8 +3271,7 @@ def calc_stable_points(self):
         """
         If the problem is one that satisfies the conditions required for target ratios of different
         variables to permanent income to exist, and has been solved to within the self-defined
-        tolerance, this method calculates the target values of market resources, consumption,
-        and assets.
+        tolerance, this method calculates the target values of market resources.
 
         Parameters
         ----------
@@ -3307,25 +3305,22 @@ def calc_stable_points(self):
                 mNrmBal = newton(func_to_zero, m0)
             except:
                 mNrmBal = np.nan
-        else:
-            mNrmBal = np.nan
-        self.solution[0].mNrmBal = mNrmBal
-
-        # If the GICMod holds, then there should be a target market resources ratio
-        if self.conditions['GICMod']:
-            # The GICMod can only hold if the GICRaw holds
+
+            # A target level of assets *might* exist even if the GICMod fails, so check no matter what
             func_to_zero = lambda m : self.Delta_mNrm_ZeroFunc(m) - cFunc(m)
-            if not np.isnan(mNrmBal):
-                m0 = mNrmBal
-            else:
-                m0 = 1.0
+            m0 = 1.0 if np.isnan(mNrmBal) else mNrmBal
             try:
-                mNrmTrg = newton(func_to_zero, m0)
+                mNrmTrg = newton(func_to_zero, m0, maxiter=200)
             except:
                 mNrmTrg = np.nan
         else:
+            mNrmBal = np.nan
             mNrmTrg = np.nan
+
+        self.solution[0].mNrmBal = mNrmBal
         self.solution[0].mNrmTrg = mNrmTrg
+        self.mNrmBal = mNrmBal
+        self.mNrmTrg = mNrmTrg