def test_ConsIndShockSolverBasic(self):
LifecycleExample = IndShockConsumerType(**init_lifecycle)
LifecycleExample.cycles = 1
LifecycleExample.solve()
# test the solution_terminal
self.assertAlmostEqual(LifecycleExample.solution[-1].cFunc(2).tolist(),
2)
self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1),
0.79429538)
self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1),
0.79391692)
self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1),
0.79253095)
self.assertAlmostEqual(LifecycleExample.solution[0].cFunc(1).tolist(),
0.7506184692092213)
self.assertAlmostEqual(LifecycleExample.solution[1].cFunc(1).tolist(),
0.7586358637239385)
self.assertAlmostEqual(LifecycleExample.solution[2].cFunc(1).tolist(),
0.7681247572911291)
solver = ConsIndShockSolverBasic(
LifecycleExample.solution[1],
LifecycleExample.IncShkDstn[0],
LifecycleExample.LivPrb[0],
LifecycleExample.DiscFac,
LifecycleExample.CRRA,
LifecycleExample.Rfree,
LifecycleExample.PermGroFac[0],
LifecycleExample.BoroCnstArt,
LifecycleExample.aXtraGrid,
LifecycleExample.vFuncBool,
LifecycleExample.CubicBool,
)
solver.prepare_to_solve()
self.assertAlmostEqual(solver.DiscFacEff, 0.9586233599999999)
self.assertAlmostEqual(solver.PermShkMinNext, 0.6554858756904397)
self.assertAlmostEqual(solver.cFuncNowCnst(4).tolist(), 4.0)
self.assertAlmostEqual(solver.prepare_to_calc_EndOfPrdvP()[0],
-0.19792871012285213)
self.assertAlmostEqual(solver.prepare_to_calc_EndOfPrdvP()[-1],
19.801071289877118)
EndOfPrdvP = solver.calc_EndOfPrdvP()
self.assertAlmostEqual(EndOfPrdvP[0], 6657.839372100613)
self.assertAlmostEqual(EndOfPrdvP[-1], 0.2606075215645896)
solution = solver.make_basic_solution(EndOfPrdvP, solver.aNrmNow,
solver.make_linear_cFunc)
solver.add_MPC_and_human_wealth(solution)
self.assertAlmostEqual(solution.cFunc(4).tolist(), 1.0028005137373956)
solver.DiscFacEff
# %%
solver.PermShkMinNext
# %% [markdown]
# These values were calculated in `setAndUpdateValues`. In `defBoroCnst` that was also called, several things were calculated, for example the consumption function defined by the borrowing constraint.
# %%
plot_funcs([solver.cFuncNowCnst], solver.mNrmMinNow, 10)
# %% [markdown]
# Then, we set up all the grids, grabs the discrete shock distributions, and state grids in `prepare_to_calc_EndOfPrdvP`.
# %%
solver.prepare_to_calc_EndOfPrdvP()
# %% [markdown]
# Then we calculate the marginal utility of next period's resources given the stochastic environment and current grids.
# %%
EndOfPrdvP = solver.calc_EndOfPrdvP()
# %% [markdown]
# Then, we essentially just have to construct the (resource, consumption) pairs by completing the EGM step, and constructing the interpolants by using the knowledge that the limiting solutions are those of the perfect foresight model. This is done with `make_basic_solution` as discussed above.
# %%
solution = solver.make_basic_solution(EndOfPrdvP, solver.aNrmNow,
solver.make_linear_cFunc)
# %% [markdown]