adding test_ConsPortfolioModel

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -1974,6 +1974,8 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False
         if self.cycles!=0 or self.T_cycle > 1:
             return
 
+        self.violated = False
+
         Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)
         self.Thorn = Thorn
 

HARK/ConsumptionSaving/tests/test_ConsPortfolioModel.py

@@ -0,0 +1,47 @@
+import copy
+import HARK.ConsumptionSaving.ConsPortfolioModel as cpm
+import HARK.ConsumptionSaving.ConsumerParameters as param
+import unittest
+
+class testPortfolioConsumerType(unittest.TestCase):
+
+    def setUp(self):
+
+        # Parameters from Mehra and Prescott (1985):
+        Avg = 1.08 # equity premium 
+        Std = 0.20 # standard deviation of rate-of-return shocks 
+
+        RiskyDstnFunc = cpm.RiskyDstnFactory(RiskyAvg=Avg, RiskyStd=Std)       # Generates nodes for integration
+        RiskyDrawFunc = cpm.LogNormalRiskyDstnDraw(RiskyAvg=Avg, RiskyStd=Std) # Function to generate draws from a lognormal distribution
+
+        init_portfolio = copy.copy(param.init_idiosyncratic_shocks) # Default parameter values for inf horiz model - including labor income with transitory and permanent shocks
+        init_portfolio['approxRiskyDstn'] = RiskyDstnFunc
+        init_portfolio['drawRiskyFunc']   = RiskyDrawFunc
+        init_portfolio['RiskyCount']      = 2   # Number of points in the approximation; 2 points is minimum
+        init_portfolio['RiskyShareCount'] = 25  # How many discrete points to allow for portfolio share
+        init_portfolio['Rfree']           = 1.0 # Riskfree return factor (interest rate is zero)
+        init_portfolio['CRRA']            = 6.0 # Relative risk aversion
+
+        # Uninteresting technical parameters:
+        init_portfolio['aXtraMax']        = 100 
+        init_portfolio['aXtraCount']      = 50
+        init_portfolio['BoroCnstArt']     = 0.0 # important for theoretical reasons
+        init_portfolio['DiscFac'] = 0.92 # Make them impatient even wrt a riskfree return of 1.08 
+
+        # Create portfolio choice consumer type
+        self.pcct = cpm.PortfolioConsumerType(**init_portfolio)
+
+        # %% {"code_folding": []}
+        # Solve the model under the given parameters
+
+        self.pcct.solve()
+
+    def test_RiskyShareFunc(self):
+
+        self.assertAlmostEqual(
+            self.pcct.solution[0].RiskyShareFunc[0][0](2).tolist(),
+            0.8796982720076255)
+
+        self.assertAlmostEqual(
+            self.pcct.solution[0].RiskyShareFunc[0][0](8).tolist(),
+            0.69738175)

HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py

@@ -1,18 +1,77 @@
 from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType
-
+import numpy as np
 import unittest
 
 class testPerfForesightConsumerType(unittest.TestCase):
 
+    def setUp(self):
+        self.agent = PerfForesightConsumerType()
+        self.agent_infinite = PerfForesightConsumerType(cycles=0)
+
     def test_default_solution(self):
-        agent = PerfForesightConsumerType()
-        agent.solve()
-        c = agent.solution[0].cFunc
+        self.agent.solve()
+        c = self.agent.solution[0].cFunc
 
         self.assertEqual(c.x_list[0], -0.9805825242718447)
         self.assertEqual(c.x_list[1], 0.01941747572815533)
         self.assertEqual(c.y_list[0], 0)
         self.assertEqual(c.y_list[1], 0.511321002804608)
         self.assertEqual(c.decay_extrap, False)
 
-
+    def test_checkConditions(self):
+        self.agent_infinite.checkConditions()
+
+        self.assertTrue(self.agent_infinite.AIC)
+        self.assertTrue(self.agent_infinite.GICPF)
+        self.assertTrue(self.agent_infinite.RIC)
+        self.assertTrue(self.agent_infinite.FHWC)
+
+    def test_simulation(self):
+
+        self.agent_infinite.solve()
+
+        # Create parameter values necessary for simulation
+        SimulationParams = {
+            "AgentCount" : 10000,  # Number of agents of this type
+            "T_sim" : 120,         # Number of periods to simulate
+            "aNrmInitMean" : -6.0, # Mean of log initial assets
+            "aNrmInitStd"  : 1.0,  # Standard deviation of log initial assets
+            "pLvlInitMean" : 0.0,  # Mean of log initial permanent income
+            "pLvlInitStd"  : 0.0,  # 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
+        }
+
+        self.agent_infinite(**SimulationParams) # This implicitly uses the assignParameters method of AgentType
+
+        # Create PFexample object
+        self.agent_infinite.track_vars = ['mNrmNow']
+        self.agent_infinite.initializeSim()
+        self.agent_infinite.simulate()
+
+        self.assertAlmostEqual(
+            np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40],
+            -23.008063500363942
+        )
+
+        self.assertAlmostEqual(
+            np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100],
+            -27.164608851546927
+        )
+
+        ## Try now with the manipulation at time step 80
+
+        self.agent_infinite.initializeSim()
+        self.agent_infinite.simulate(80)
+        self.agent_infinite.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5
+        self.agent_infinite.simulate(40)
+
+        self.assertAlmostEqual(
+            np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[40],
+            -23.008063500363942
+        )
+
+        self.assertAlmostEqual(
+            np.mean(self.agent_infinite.mNrmNow_hist,axis=1)[100],
+            -29.140261331951606
+        )