for cyclical models, correct order for input params to correspond to t,

econ-ark#1022

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -1729,7 +1729,7 @@ def sim_death(self):
         # Determine who dies
         DiePrb_by_t_cycle = 1.0 - np.asarray(self.LivPrb)
         DiePrb = DiePrb_by_t_cycle[
-            self.t_cycle - 1
+            self.t_cycle - 1 if self.cycles == 1 else self.t_cycle
         ]  # Time has already advanced, so look back one
 
         # In finite-horizon problems the previous line gives newborns the
@@ -3047,7 +3047,7 @@ def construct_assets_grid(parameters):
 
 # Make a dictionary to specify an infinite consumer with a four period cycle
 init_cyclical = copy(init_idiosyncratic_shocks)
-init_cyclical['PermGroFac'] = [1.082251, 2.8, 0.3, 1.1]
+init_cyclical['PermGroFac'] = [1.1, 1.082251, 2.8, 0.3]
 init_cyclical['PermShkStd'] = [0.1, 0.1, 0.1, 0.1]
 init_cyclical['TranShkStd'] = [0.1, 0.1, 0.1, 0.1]
 init_cyclical['LivPrb'] = 4*[0.98]

HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py

@@ -313,7 +313,7 @@ def test_lifecyle(self):
     "Rfree": 1.03,  # Interest factor on assets
     "DiscFac": 0.96,  # Intertemporal discount factor
     "LivPrb": 4 * [0.98],  # Survival probability
-    "PermGroFac": [1.082251, 2.8, 0.3, 1.1],
+    "PermGroFac": [1.1, 1.082251, 2.8, 0.3],
     # Parameters that specify the income distribution over the lifecycle
     "PermShkStd": [0.1, 0.1, 0.1, 0.1],
     "PermShkCount": 7,  # Number of points in discrete approximation to permanent income shocks

HARK/core.py

@@ -1004,10 +1004,12 @@ def solve_one_cycle(agent, solution_last):
     # Initialize the solution for this cycle, then iterate on periods
     solution_cycle = []
     solution_next = solution_last
-    for t in range(T):
+
+    cycles_range = [0] + list(range(T - 1, 0, -1))
+    for k in (range(T-1, -1, -1) if agent.cycles == 1 else cycles_range):
         # Update which single period solver to use (if it depends on time)
         if hasattr(agent.solve_one_period, "__getitem__"):
-            solve_one_period = agent.solve_one_period[T - 1 - t]
+            solve_one_period = agent.solve_one_period[k]
         else:
             solve_one_period = agent.solve_one_period
 
@@ -1019,8 +1021,7 @@ def solve_one_cycle(agent, solution_last):
         # Update time-varying single period inputs
         for name in agent.time_vary:
             if name in these_args:
-                # solve_dict[name] = eval('agent.' + name + '[t]')
-                solve_dict[name] = agent.__dict__[name][T - 1 - t]
+                solve_dict[name] = agent.__dict__[name][k]
         solve_dict["solution_next"] = solution_next
 
         # Make a temporary dictionary for this period