Merge pull request #1181 from Mv77/plumbing/mkshks_bugfix

[Bug-fix] Mortality and `t_cycle` when shocks are read

Documentation/CHANGELOG.md

@@ -30,6 +30,7 @@ Release Date: TBD
 * Adds support for the calculation of dreivatives in the `interpolation.py` wrappers. [#1157](https://github.com/econ-ark/HARK/pull/1157)
 * Adds class `DecayInterp` to `econforgeinterp.py`. It implements interpolators that "decay" to some limiting function when extrapolating. [#1165](https://github.com/econ-ark/HARK/pull/1165)
 * Add methods to non stochastically simulate an economy by computing transition matrices. Functions to compute transition matrices and ergodic distribution have been added [#1155](https://github.com/econ-ark/HARK/pull/1155).
+* Fixes a bug that causes `t_age` and `t_cycle` to get out of sync when reading pre-computed mortality. [#1181](https://github.com/econ-ark/HARK/pull/1181)
 
 ### Minor Changes
 

HARK/Calibration/Income/IncomeTools.py

@@ -31,7 +31,7 @@
 def parse_time_params(age_birth, age_death):
     """
     Converts simple statements of the age at which an agent is born and the
-    age at which he dies with certaintiy into the parameters that HARK needs
+    age at which he dies with certainty into the parameters that HARK needs
     for figuring out the timing of the model.
 
     Parameters
@@ -52,7 +52,7 @@ def parse_time_params(age_birth, age_death):
     T_cycle = age_death - age_birth
     # T_age is the age at which the agents are killed with certainty in
     # simulations (at the end of the T_age-th period)
-    T_age = age_death - age_birth + 1
+    T_age = age_death - age_birth
 
     return {"T_cycle": T_cycle, "T_age": T_age}
 

HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py

@@ -773,6 +773,81 @@ def test_NewbornStatesAndShocks(self):
         )
 
 
+class testLCMortalityReadShocks(unittest.TestCase):
+    """
+    Tests that mortality is working adequately when shocks are read
+    """
+
+    def setUp(self):
+        # Make a dictionary containing all parameters needed to solve the model
+        self.base_params = copy(init_lifecycle)
+
+        agent_count = 10
+        t_sim = 2000
+
+        self.base_params.update(
+            {
+                "AgentCount": agent_count,
+                "T_sim": t_sim,
+                "track_vars": ["t_age", "t_cycle"],
+            }
+        )
+
+    def test_compare_t_age_t_cycle(self):
+
+        # Make agent, shock and initial condition histories
+        agent = IndShockConsumerType(**self.base_params)
+        agent.make_shock_history()
+
+        # Solve and simulate the agent
+        agent.solve()
+        agent.initialize_sim()
+        agent.simulate()
+
+        hist = copy(agent.history)
+        for key, array in hist.items():
+            hist[key] = array.flatten(order="F")
+
+        # Check that t_age is always t_cycle
+        # Except possibly in cases where the agent reach t_age = T_age. In this case,
+        # t_cycle is set to 0 at the end of the period, and the agent dies,
+        # But t_age is reset only at the start of next period and thus we can have
+        # t_age = T_age and t_cycle = 0
+        self.assertTrue(
+            np.all(
+                np.logical_or(
+                    hist["t_age"] == hist["t_cycle"],
+                    np.logical_and(
+                        hist["t_cycle"] == 0, hist["t_age"] == agent.T_cycle
+                    ),
+                )
+            )
+        )
+
+    def test_compare_t_age_t_cycle_premature_death(self):
+
+        # Re-do the previous test in an instance where we prematurely
+        # kill agents
+        par = copy(self.base_params)
+        par["T_age"] = par["T_age"] - 8
+        # Make agent, shock and initial condition histories
+        agent = IndShockConsumerType(**par)
+        agent.make_shock_history()
+
+        # Solve and simulate the agent
+        agent.solve()
+        agent.initialize_sim()
+        agent.simulate()
+
+        hist = copy(agent.history)
+        for key, array in hist.items():
+            hist[key] = array.flatten(order="F")
+
+        # Check that t_age is always t_cycle
+        # (the exception from before should not happen
+        # because we are killing agents before T_cycle)
+        self.assertTrue(np.all(hist["t_age"] == hist["t_cycle"]))
+
 #%% Test Transition Matrix Methods
 
 

HARK/core.py

@@ -747,6 +747,7 @@ def get_mortality(self):
 
                 # Reset ages of newborns
                 self.t_age[who_dies] = 0
+                self.t_cycle[who_dies] = 0
         else:
             who_dies = self.sim_death()
             self.sim_birth(who_dies)