Expand SSJ exposition

HARK/ConsumptionSaving/ConsNewKeynesianModel.py

@@ -59,24 +59,24 @@
 # Default parameters to make aXtraGrid using make_assets_grid
 default_aXtraGrid_params = {
     "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
-    "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
+    "aXtraMax": 50,  # Maximum end-of-period "assets above minimum" value
     "aXtraNestFac": 3,  # Exponential nesting factor for aXtraGrid
-    "aXtraCount": 48,  # Number of points in the grid of "assets above minimum"
+    "aXtraCount": 100,  # Number of points in the grid of "assets above minimum"
     "aXtraExtra": None,  # Additional other values to add in grid (optional)
 }
 
 # Make a dictionary to specify an idiosyncratic income shocks consumer type
 init_newkeynesian = {
     # BASIC HARK PARAMETERS REQUIRED TO SOLVE THE MODEL
-    "cycles": 1,  # Finite, non-cyclic model
+    "cycles": 0,  # Infinite horizon model
     "T_cycle": 1,  # Number of periods in the cycle for this agent type
     "constructors": newkeynesian_constructor_dict,  # See dictionary above
     # PRIMITIVE RAW PARAMETERS REQUIRED TO SOLVE THE MODEL
     "CRRA": 2.0,  # Coefficient of relative risk aversion
     "Rfree": 1.03,  # Interest factor on retained assets
     "DiscFac": 0.96,  # Intertemporal discount factor
     "LivPrb": [0.98],  # Survival probability after each period
-    "PermGroFac": [1.01],  # Permanent income growth factor
+    "PermGroFac": [1.0],  # Permanent income growth factor
     "BoroCnstArt": 0.0,  # Artificial borrowing constraint
     "vFuncBool": False,  # Whether to calculate the value function during solution
     "CubicBool": False,  # Whether to use cubic spline interpolation when True
@@ -95,6 +95,11 @@
     "PerfMITShk": False,  # Do Perfect Foresight MIT Shock
     # (Forces Newborns to follow solution path of the agent they replaced if True)
     "neutral_measure": False,  # Whether to use permanent income neutral measure (see Harmenberg 2021)
+    # ADDITIONAL PARAMETERS FOR GRID-BASED TRANSITION SIMULATION
+    "mMin": 0.001,
+    "mMax": 50,
+    "mCount": 200,
+    "mFac": 3,
 }
 init_newkeynesian.update(default_IncShkDstn_params)
 init_newkeynesian.update(default_aXtraGrid_params)

HARK/ConsumptionSaving/tests/test_ConsNewKeynesianModel.py

@@ -5,59 +5,15 @@
 from HARK.ConsumptionSaving.ConsNewKeynesianModel import NewKeynesianConsumerType
 from HARK.tests import HARK_PRECISION
 
-jacobian_test_dict = {
-    # Parameters shared with the perfect foresight model
-    "CRRA": 2,  # Coefficient of relative risk aversion
-    "Rfree": 1.04**0.25,  # Interest factor on assets
-    "DiscFac": 0.9735,  # Intertemporal discount factor
-    "LivPrb": [0.99375],  # Survival probability
-    "PermGroFac": [1.00],  # Permanent income growth factor
-    # Parameters that specify the income distribution over the lifecycle
-    "PermShkStd": [
-        0.06
-    ],  # [(0.01*4/11)**0.5],    # Standard deviation of log permanent shocks to income
-    "PermShkCount": 5,  # Number of points in discrete approximation to permanent income shocks
-    "TranShkStd": [0.3],  # Standard deviation of log transitory shocks to income
-    "TranShkCount": 5,  # Number of points in discrete approximation to transitory income shocks
-    "UnempPrb": 0.07,  # Probability of unemployment while working
-    "IncUnemp": 0.3,  # Unemployment benefits replacement rate
-    "UnempPrbRet": 0.0005,  # Probability of "unemployment" while retired
-    "IncUnempRet": 0.0,  # "Unemployment" benefits when retired
-    "T_retire": 0,  # Period of retirement (0 --> no retirement)
-    # Parameters for constructing the "assets above minimum" grid
-    "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
-    "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
-    "aXtraCount": 48,  # Number of points in the base grid of "assets above minimum"
-    "aXtraNestFac": 3,  # Exponential nesting factor when constructing "assets above minimum" grid
-    "aXtraExtra": None,  # Additional values to add to aXtraGrid
-    # A few other parameters
-    "BoroCnstArt": 0.0,  # Artificial borrowing constraint; imposed minimum level of end-of period assets
-    "vFuncBool": True,  # Whether to calculate the value function during solution
-    "CubicBool": False,  # Preference shocks currently only compatible with linear cFunc
-    "T_cycle": 1,  # Number of periods in the cycle for this agent type
-    # Parameters only used in simulation
-    "AgentCount": 500,  # Number of agents of this type
-    "T_sim": 100,  # Number of periods to simulate
-    "aNrmInitMean": np.log(1.3) - (0.5**2) / 2,  # Mean of log initial assets
-    "aNrmInitStd": 0.5,  # Standard deviation of log initial assets
-    "pLvlInitMean": 0,  # Mean of log initial permanent income
-    "pLvlInitStd": 0,  # Standard deviation of log initial permanent income
-    "PermGroFacAgg": 1.0,  # Aggregate permanent income growth factor
-    "T_age": None,  # Age after which simulated agents are automatically killed
-    # Parameters for Transition Matrix Simulation
-    "mMin": 0.001,
-    "mMax": 20,
-    "mCount": 48,
-    "mFac": 3,
-}
-
 
 # %% Test Transition Matrix Methods
 
+# Uses class default values
+
 
 class test_Transition_Matrix_Methods(unittest.TestCase):
     def test_calc_tran_matrix(self):
-        example1 = NewKeynesianConsumerType(**jacobian_test_dict)
+        example1 = NewKeynesianConsumerType()
         example1.cycles = 0
         example1.solve()
 
@@ -83,19 +39,19 @@ def test_calc_tran_matrix(self):
         AggC = np.dot(gridc.flatten(), vecDstn)  # Aggregate Consumption
         AggA = np.dot(grida.flatten(), vecDstn)  # Aggregate Assets
 
-        self.assertAlmostEqual(AggA[0], 1.19513, places=4)
-        self.assertAlmostEqual(AggC[0], 1.00417, places=4)
+        self.assertAlmostEqual(AggA[0], 0.82984, places=4)
+        self.assertAlmostEqual(AggC[0], 1.00780, places=4)
 
 
 # %% Test Heterogenous Agent Jacobian Methods
 
 
 class test_Jacobian_methods(unittest.TestCase):
     def test_calc_jacobian(self):
-        Agent = NewKeynesianConsumerType(**jacobian_test_dict)
+        Agent = NewKeynesianConsumerType()
         Agent.compute_steady_state()
         CJAC_Perm, AJAC_Perm = Agent.calc_jacobian("PermShkStd", 50)
 
-        self.assertAlmostEqual(CJAC_Perm.T[30][29], -0.06120, places=HARK_PRECISION)
-        self.assertAlmostEqual(CJAC_Perm.T[30][30], 0.05307, places=HARK_PRECISION)
-        self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.04674, places=HARK_PRECISION)
+        self.assertAlmostEqual(CJAC_Perm.T[30][29], -0.10503, places=HARK_PRECISION)
+        self.assertAlmostEqual(CJAC_Perm.T[30][30], 0.10316, places=HARK_PRECISION)
+        self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.09059, places=HARK_PRECISION)