adding simulation test for PerfForesightConsumerType

HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py

@@ -1,5 +1,5 @@
 from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType
-
+import numpy as np
 import unittest
 
 class testPerfForesightConsumerType(unittest.TestCase):
@@ -25,3 +25,53 @@ def test_checkConditions(self):
         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
+        )