do_ALL.py

# Runtime ~1300 seconds
import sys
sys.path.append('./Code/Python/')

# %% Set up plot displays
import matplotlib
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'auto')

# %% Calibration assessment and life cycle simulations

# 1. Solve the model and display its policy functions
print('1. Solve the model and display its policy functions')
import Simulations.PolicyFuncs

# 2. Simulate the lives of a few agents to show the implied income
# and stockholding processes.
print('2. Simulate the lives of a few agents to show the implied income and stockholding processes.')
import Simulations.FewAgents

# 3. Run a larger simulation to display the age conditional means of variables
# of interest.
print('3. Run a larger simulation to display the age conditional means of variables of interest.')
import Simulations.AgeMeans

# %% Comparison

# 4. Solve and compare policy functions with those obtained from CGM's
# Fortran 90 code
print('4. Solve and compare policy functions with those obtained from CGM\'s Fortran 90 code')
import Comparison.ComparePolFuncs

# 5. Present more detailed figures on discrepancies for the last periods of
# life.
print('5. Present more detailed figures on discrepancies for the last periods of life.')
import Comparison.Compare_last_periods

# %% Appendix

# 6. Use HARK to compare the limiting MPC to the theoretical result obtained
# when there is no income risk and no riskless asset.
print('6. Use HARK to compare the limiting MPC to the theoretical result obtained when there is no income risk and no riskless asset.')
import Appendix.MPCLimit

# 7. Turn off all shocks and check if consumption converges to its analytical
# perfect foresight solution
print('7. Turn off all shocks and check if consumption converges to its analytical perfect foresight solution')
import Appendix.PF_analytical_sol