gistfile1.txt

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [39], in <cell line: 1>()
----> 1 compute_target_wealth()

Input In [38], in compute_target_wealth(CRRA, DiscFac, RiskyAvg, RiskyStd, PermShkStd, PermGroFac, UnempPrb)
     22 agent = SequentialPortfolioConsumerType(**agent_parameters)
     23 pprint(agent.parameters)
---> 24 agent.solve()
     26 ## subjective return
     27 srle1 = price_dividend_ratio_random_walk(DiscFac, CRRA)

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/core.py:287, in AgentType.solve(self, verbose)
    283 with np.errstate(
    284     divide="ignore", over="ignore", under="ignore", invalid="ignore"
    285 ):
    286     self.pre_solve()  # Do pre-solution stuff
--> 287     self.solution = solve_agent(
    288         self, verbose
    289     )  # Solve the model by backward induction
    290     self.post_solve()

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/core.py:895, in solve_agent(agent, verbose)
    892     t_last = time()
    893 while go:
    894     # Solve a cycle of the model, recording it if horizon is finite
--> 895     solution_cycle = solve_one_cycle(agent, solution_last)
    896     if not infinite_horizon:
    897         solution = solution_cycle + solution

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/core.py:1019, in solve_one_cycle(agent, solution_last)
   1016 temp_dict = {name: solve_dict[name] for name in these_args}
   1018 # Solve one period, add it to the solution, and move to the next period
-> 1019 solution_t = solve_one_period(**temp_dict)
   1020 solution_cycle.insert(0, solution_t)
   1021 solution_next = solution_t

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/core.py:1047, in make_one_period_oo_solver.<locals>.one_period_solver(**kwds)
   1044 if hasattr(solver, "prepare_to_solve"):
   1045     solver.prepare_to_solve()
-> 1047 solution_now = solver.solve()
   1048 return solution_now

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/ConsumptionSaving/ConsPortfolioModel.py:1252, in ConsSequentialPortfolioSolver.solve(self)
   1251 def solve(self):
-> 1252     solution = ConsPortfolioSolver.solve(self)
   1254     solution = self.add_SequentialShareFuncAdj(solution)
   1256     return solution

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/ConsumptionSaving/ConsPortfolioModel.py:913, in ConsPortfolioSolver.solve(self)
    910 self.calc_EndOfPrdvP()
    912 # Construct a basic solution for this period
--> 913 self.optimize_share()
    914 self.make_basic_solution()
    915 self.make_ShareFuncAdj()

File ~/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/HARK/ConsumptionSaving/ConsPortfolioModel.py:681, in ConsPortfolioSolver.optimize_share(self)
    679 for j in range(self.aNrmCount):
    680     if not (constrained_top[j] or constrained_bot[j]):
--> 681         idx = np.argwhere(crossing[j, :])[0][0]
    682         bot_s = self.ShareGrid[idx]
    683         top_s = self.ShareGrid[idx + 1]

IndexError: index 0 is out of bounds for axis 0 with size 0

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