fix kink in cons Bequest model

HARK/ConsumptionSaving/ConsBequestModel.py

@@ -1,5 +1,4 @@
-"""
-Classes to solve consumption-saving models with a bequest motive and
+"""Classes to solve consumption-saving models with a bequest motive and
 idiosyncratic shocks to income and wealth. All models here assume
 separable CRRA utility of consumption and Stone-Geary utility of
 savings with geometric discounting of the continuation value and
@@ -11,7 +10,6 @@
 """
 
 import numpy as np
-
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsIndShockSolver,
     IndShockConsumerType,
@@ -37,51 +35,44 @@
 
 
 class BequestWarmGlowConsumerType(IndShockConsumerType):
-    time_vary_ = IndShockConsumerType.time_vary_ + [
+    time_inv_ = IndShockConsumerType.time_inv_ + [
         "BeqCRRA",
-        "BeqFac",
         "BeqShift",
     ]
 
+    time_vary_ = IndShockConsumerType.time_vary_ + [
+        "BeqFac",
+    ]
+
     def __init__(self, **kwds):
         params = init_wealth_in_utility.copy()
         params.update(kwds)
 
         super().__init__(**params)
 
-        self.solve_one_period = make_one_period_oo_solver(BequestWarmGlowConsumerSolver)
+        self.solve_one_period = make_one_period_oo_solver(
+            BequestWarmGlowConsumerSolver,
+        )
 
     def update(self):
         super().update()
         self.update_parameters()
 
     def update_parameters(self):
-        if isinstance(self.BeqCRRA, (int, float)):
-            self.BeqCRRA = [self.BeqCRRA] * self.T_cycle
-        elif len(self.BeqCRRA) == 1:
-            self.BeqCRRA *= self.T_cycle
-        elif len(self.BeqCRRA) != self.T_cycle:
-            raise ValueError(
-                "Bequest CRRA parameter must be a single value or a list of length T_cycle"
-            )
+        if not isinstance(self.BeqCRRA, (int, float)):
+            raise ValueError("Bequest CRRA parameter must be a single value.")
 
         if isinstance(self.BeqFac, (int, float)):
             self.BeqFac = [self.BeqFac] * self.T_cycle
         elif len(self.BeqFac) == 1:
             self.BeqFac *= self.T_cycle
         elif len(self.BeqFac) != self.T_cycle:
             raise ValueError(
-                "Bequest relative value parameter must be a single value or a list of length T_cycle"
+                "Bequest relative value parameter must be a single value or a list of length T_cycle",
             )
 
-        if isinstance(self.BeqShift, (int, float)):
-            self.BeqShift = [self.BeqShift] * self.T_cycle
-        elif len(self.BeqShift) == 1:
-            self.BeqShift *= self.T_cycle
-        elif len(self.BeqShift) != self.T_cycle:
-            raise ValueError(
-                "Bequest Stone-Geary parameter must be a single value or a list of length T_cycle"
-            )
+        if not isinstance(self.BeqShift, (int, float)):
+            raise ValueError("Bequest Stone-Geary parameter must be a single value.")
 
     def update_solution_terminal(self):
         if self.TermBeqFac == 0.0:  # No terminal bequest
@@ -90,7 +81,9 @@ def update_solution_terminal(self):
             utility = UtilityFuncCRRA(self.CRRA)
 
             warm_glow = UtilityFuncStoneGeary(
-                self.TermBeqCRRA, factor=self.TermBeqFac, shifter=self.TermBeqShift
+                self.TermBeqCRRA,
+                factor=self.TermBeqFac,
+                shifter=self.TermBeqShift,
             )
 
             aNrmGrid = (
@@ -127,7 +120,7 @@ def __init__(self, **kwds):
         super().__init__(**params)
 
         self.solve_one_period = make_one_period_oo_solver(
-            BequestWarmGlowPortfolioSolver
+            BequestWarmGlowPortfolioSolver,
         )
 
     def update(self):
@@ -212,6 +205,28 @@ def def_utility_funcs(self):
 
         self.warm_glow = UtilityFuncStoneGeary(self.BeqCRRA, BeqFacEff, self.BeqShift)
 
+    def def_BoroCnst(self, BoroCnstArt):
+        self.BoroCnstNat = (
+            (self.solution_next.mNrmMin - self.TranShkMinNext)
+            * (self.PermGroFac * self.PermShkMinNext)
+            / self.Rfree
+        )
+
+        self.BoroCnstNat = np.max([self.BoroCnstNat, -self.BeqShift])
+
+        if BoroCnstArt is None:
+            self.mNrmMinNow = self.BoroCnstNat
+        else:
+            self.mNrmMinNow = np.max([self.BoroCnstNat, BoroCnstArt])
+        if self.BoroCnstNat < self.mNrmMinNow:
+            self.MPCmaxEff = 1.0
+        else:
+            self.MPCmaxEff = self.MPCmaxNow
+
+        self.cFuncNowCnst = LinearInterp(
+            np.array([self.mNrmMinNow, self.mNrmMinNow + 1]), np.array([0.0, 1.0])
+        )
+
     def calc_EndOfPrdvP(self):
         EndofPrdvP = super().calc_EndOfPrdvP()
 

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -12,19 +12,10 @@
 See NARK https://github.com/econ-ark/HARK/blob/master/Documentation/NARK/NARK.pdf for information on variable naming conventions.
 See HARK documentation for mathematical descriptions of the models being solved.
 """
+
 from copy import copy, deepcopy
 
 import numpy as np
-from scipy import sparse as sp
-from scipy.optimize import newton
-
-from HARK import (
-    AgentType,
-    NullFunc,
-    _log,
-    make_one_period_oo_solver,
-    set_verbosity_level,
-)
 from HARK.Calibration.Income.IncomeTools import (
     Cagetti_income,
     parse_income_spec,
@@ -71,6 +62,16 @@
     jump_to_grid_2D,
     make_grid_exp_mult,
 )
+from scipy import sparse as sp
+from scipy.optimize import newton
+
+from HARK import (
+    AgentType,
+    NullFunc,
+    _log,
+    make_one_period_oo_solver,
+    set_verbosity_level,
+)
 
 __all__ = [
     "ConsumerSolution",
@@ -2221,8 +2222,8 @@ def check_conditions(self, verbose=None):
 
 
 # Make a dictionary to specify an idiosyncratic income shocks consumer
-init_idiosyncratic_shocks = dict(
-    init_perfect_foresight,
+init_idiosyncratic_shocks = {
+    **init_perfect_foresight,
     **{  # assets above grid parameters
         "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
         "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
@@ -2255,7 +2256,7 @@ def check_conditions(self, verbose=None):
         # Whether Newborns have transitory shock. The default is False.
         "NewbornTransShk": False,
     },
-)
+}
 
 
 class IndShockConsumerType(PerfForesightConsumerType):