diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 73722e108..e24fd84ee 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -14,6 +14,7 @@ Release Data: TBD
 
 #### Major Changes
 
+* Input parameters for cyclical models now indexed by t [#1039](https://github.com/econ-ark/HARK/pull/1039)
 * A IndexDistribution class for representing time-indexed probability distributions [#1018](https://github.com/econ-ark/pull/1018/).
 * Adds new consumption-savings-portfolio model `RiskyContrib`, which represents an agent who can save in risky and risk-free assets but faces
 frictions to moving funds between them. To circumvent these frictions, he has access to an income-deduction scheme to accumulate risky assets.
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 892737bbe..28f601c62 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/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
@@ -2173,12 +2173,17 @@ def get_shocks(self):
         newborn = self.t_age == 0
         for t in range(self.T_cycle):
             these = t == self.t_cycle
+
+            # temporary, see #1022
+            if self.cycles == 1:
+                t = t - 1
+
             N = np.sum(these)
             if N > 0:
                 IncShkDstnNow = self.IncShkDstn[
-                    t - 1
+                    t
                 ]  # set current income distribution
-                PermGroFacNow = self.PermGroFac[t - 1]  # and permanent growth factor
+                PermGroFacNow = self.PermGroFac[t]  # and permanent growth factor
                 # Get random draws of income shocks from the discrete distribution
                 IncShks = IncShkDstnNow.draw(N)
 
@@ -3047,7 +3052,7 @@ 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]
diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
index aae33d65a..c2f500020 100644
--- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
+++ b/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
@@ -358,6 +358,11 @@ def test_cyclical(self):
             CyclicalExample.solution[3].cFunc(3).tolist(), 1.5958390056965004
         )
 
+        CyclicalExample.initialize_sim()
+        CyclicalExample.simulate()
+
+        self.assertAlmostEqual(CyclicalExample.state_now['aLvl'][1], 0.41839957)
+
 # %% Tests of 'stable points'
 
 
diff --git a/HARK/core.py b/HARK/core.py
index 8f00c43b8..557755d69 100644
--- a/HARK/core.py
+++ b/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