Update ConsIndShock's init_lifecycle

Documentation/CHANGELOG.md

@@ -30,6 +30,7 @@ interpolation for problems with CRRA utility. See [#888](https://github.com/econ
 * Adds a module with tools for parsing and using various income calibrations from the literature. It includes the option of using life-cycle profiles of income shock variances from [Sabelhaus and Song (2010)](https://www.sciencedirect.com/science/article/abs/pii/S0304393210000358). See [#921](https://github.com/econ-ark/HARK/pull/921), [#941](https://github.com/econ-ark/HARK/pull/941).
 * remove "Now" from model variable names [#936](https://github.com/econ-ark/HARK/pull/936)
 * Moves state MrkvNow to shocks['Mrkv'] in AggShockMarkov and KrusellSmith models [#935](https://github.com/econ-ark/HARK/pull/935)
+* Replaces `ConsIndShock`'s `init_lifecycle` with an actual life-cycle calibration [#951](https://github.com/econ-ark/HARK/pull/951).
 
 #### Minor Changes
 

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -49,6 +49,9 @@
 from HARK import _log
 from HARK import set_verbosity_level
 
+from HARK.Calibration.Income.IncomeTools import ParseIncomeSpec, ParseTimeParams, Cagetti_income
+from HARK.datasets.SCF.WealthIncomeDist.SCFDistTools import income_wealth_dists_from_scf
+from HARK.datasets.life_tables.us_ssa.SSATools import parse_ssa_life_table
 
 __all__ = [
     "ConsumerSolution",
@@ -2825,27 +2828,46 @@ def constructAssetsGrid(parameters):
 
 
 # Make a dictionary to specify a lifecycle consumer with a finite horizon
+
+# Main calibration characteristics
+birth_age = 25
+death_age = 90
+adjust_infl_to = 1992
+# Use income estimates from Cagetti (2003) for High-school graduates
+education = "HS"
+income_calib = Cagetti_income[education]
+
+# Income specification
+income_params = ParseIncomeSpec(
+    age_min=birth_age,
+    age_max=death_age,
+    adjust_infl_to=adjust_infl_to,
+    **income_calib,
+    SabelhausSong=True
+)
+
+# Initial distribution of wealth and permanent income
+dist_params = income_wealth_dists_from_scf(
+    base_year=adjust_infl_to, age=birth_age, education=education, wave=1995
+)
+
+# We need survival probabilities only up to death_age-1, because survival
+# probability at death_age is 1.
+liv_prb = parse_ssa_life_table(
+    female=False, cross_sec=True, year=2004, min_age=birth_age, max_age=death_age - 1
+)
+
+# Parameters related to the number of periods implied by the calibration
+time_params = ParseTimeParams(age_birth=birth_age, age_death=death_age)
+
+# Update all the new parameters
 init_lifecycle = copy(init_idiosyncratic_shocks)
-init_lifecycle["PermGroFac"] = [
-    1.01,
-    1.01,
-    1.01,
-    1.01,
-    1.01,
-    1.02,
-    1.02,
-    1.02,
-    1.02,
-    1.02,
-]
-init_lifecycle["PermShkStd"] = [0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0, 0, 0]
-init_lifecycle["TranShkStd"] = [0.3, 0.2, 0.1, 0.3, 0.2, 0.1, 0.3, 0, 0, 0]
-init_lifecycle["LivPrb"] = [0.99, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
-init_lifecycle["T_cycle"] = 10
-init_lifecycle["T_retire"] = 7
-init_lifecycle[
-    "T_age"
-] = 11  # Make sure that old people die at terminal age and don't turn into newborns!
+init_lifecycle.update(time_params)
+init_lifecycle.update(dist_params)
+# Note the income specification overrides the pLvlInitMean from the SCF.
+init_lifecycle.update(income_params)
+init_lifecycle.update({"LivPrb": liv_prb})
+
 
 # Make a dictionary to specify an infinite consumer with a four period cycle
 init_cyclical = copy(init_idiosyncratic_shocks)

HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py

@@ -33,20 +33,20 @@ def test_ConsIndShockSolverBasic(self):
         LifecycleExample.solve()
 
         # test the solution_terminal
-        self.assertAlmostEqual(LifecycleExample.solution[10].cFunc(2).tolist(), 2)
+        self.assertAlmostEqual(LifecycleExample.solution[-1].cFunc(2).tolist(), 2)
 
-        self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1), 0.97769632)
-        self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1), 0.96624445)
-        self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1), 0.95691449)
+        self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1), 0.89066194)
+        self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1), 0.89144313)
+        self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1), 0.89210133)
 
         self.assertAlmostEqual(
-            LifecycleExample.solution[0].cFunc(1).tolist(), 0.87362789
+            LifecycleExample.solution[0].cFunc(1).tolist(), 0.8928547282397321
         )
         self.assertAlmostEqual(
-            LifecycleExample.solution[1].cFunc(1).tolist(), 0.9081621
+            LifecycleExample.solution[1].cFunc(1).tolist(), 0.8930303445748624
         )
         self.assertAlmostEqual(
-            LifecycleExample.solution[2].cFunc(1).tolist(), 0.9563899
+            LifecycleExample.solution[2].cFunc(1).tolist(), 0.8933075371183773
         )
 
         solver = ConsIndShockSolverBasic(
@@ -65,23 +65,23 @@ def test_ConsIndShockSolverBasic(self):
 
         solver.prepareToSolve()
 
-        self.assertAlmostEqual(solver.DiscFacEff, 0.9503999999999999)
-        self.assertAlmostEqual(solver.PermShkMinNext, 0.850430160026919)
+        self.assertAlmostEqual(solver.DiscFacEff, 0.9586233599999999)
+        self.assertAlmostEqual(solver.PermShkMinNext, 0.9037554719886154)
         self.assertAlmostEqual(solver.cFuncNowCnst(4).tolist(), 4.0)
-        self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[0], -0.2491750859108316)
-        self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[-1], 19.74982491408914)
+        self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[0], -0.2732742703949109)
+        self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[-1], 19.72572572960506)
 
         EndOfPrdvP = solver.calcEndOfPrdvP()
 
-        self.assertAlmostEqual(EndOfPrdvP[0], 6622.251864311334)
-        self.assertAlmostEqual(EndOfPrdvP[-1], 0.026301061207747087)
+        self.assertAlmostEqual(EndOfPrdvP[0], 6710.672670733023)
+        self.assertAlmostEqual(EndOfPrdvP[-1], 0.14122987153089447)
 
         solution = solver.makeBasicSolution(
             EndOfPrdvP, solver.aNrmNow, solver.makeLinearcFunc
         )
         solver.addMPCandHumanWealth(solution)
 
-        self.assertAlmostEqual(solution.cFunc(4).tolist(), 1.7391265696400773)
+        self.assertAlmostEqual(solution.cFunc(4).tolist(), 1.484118342351686)
 
     def test_simulated_values(self):
         self.agent.initializeSim()

HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py

@@ -37,20 +37,20 @@ def test_ConsIndShockSolverBasic(self):
         LifecycleExample.solve()
 
         # test the solution_terminal
-        self.assertAlmostEqual(LifecycleExample.solution[10].cFunc(2).tolist(), 2)
+        self.assertAlmostEqual(LifecycleExample.solution[-1].cFunc(2).tolist(), 2)
 
-        self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1), 0.97769632)
-        self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1), 0.96624445)
-        self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1), 0.95691449)
+        self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1), 0.89066194)
+        self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1), 0.89144313)
+        self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1), 0.89210133)
 
         self.assertAlmostEqual(
-            LifecycleExample.solution[0].cFunc(1).tolist(), 0.87362789
+            LifecycleExample.solution[0].cFunc(1).tolist(), 0.8928547282397321
         )
         self.assertAlmostEqual(
-            LifecycleExample.solution[1].cFunc(1).tolist(), 0.9081621
+            LifecycleExample.solution[1].cFunc(1).tolist(), 0.8930303445748624
         )
         self.assertAlmostEqual(
-            LifecycleExample.solution[2].cFunc(1).tolist(), 0.9563899
+            LifecycleExample.solution[2].cFunc(1).tolist(), 0.8933075371183773
         )
 
     def test_simulated_values(self):