No Short in Sight

Paul Ehling, Christian Heyerdahl-Larsen, and Zeshu XU

Install dependencies

pip install -r requirements.txt

Execute the cohort simulation

python3 main.py

Linter

To have a consistent coding style, use black to lint the code before commit

black main.py

Function descriptions

1. param.py defines the parameters used in the simulations, and prepares matrix to store the results:
   1. additionally, param_mix.py defines the parameters used for the mix scenario;
2. stats.py defines some base functions used repeatedly throughout the simulations, including:
   1. post_var(): calculate the posterior variance, correspond to eq(6);
   2. shocks(): calculate Zt and Yt from shocks dZt;
   3. tau_calculator(): calculate tau for the cohorts in the economy;
3. solver.py defines some base solver functions used repeatedly throughout the simulations, including:
   1. bisection(): Bisection method to solve for any the solution in any equation;
   2. solve_theta(): combines with bisection(), contains RHS - LHS of the eq(22), used to iteratively solve market-clearing price of risk, ie. theta;
   3. find_the_rich(): finds the agents that make the top x% the richest population in the economy, and they can short;
   4. bisection_partial_constraint() and solve_theta_partial_constraint() are similar to 3.i and 3.ii, but takes more arguments;
4. cohort_builder.py defines functions that build up an OLG economy:
   1. build_cohorts_SI(): mainly for the reentry scenario;
   2. build_cohorts_mix_type(): mainly for the mix scenario;
5. cohort_simulator.py defines functions that simulate the OLG economy forward:
   1. simulate_cohorts_SI(): mainly for the reentry scenario;
   2. simulate_cohorts_mean_vola(): mainly for the reentry scenario, saves only time-series mean and variance; 3 simulate_cohorts_mix_type(): mainly for the mix scenario;
   3. simulate_mean_vola_mix_type(): mainly for the mix scenario, saves only time-series mean and variance;
6. simulation.py defines functions that bundle 4 and 5 in one function (SI, mean_vola, SI_mix, mean_vola_mix);
7. main_multiprocessing.py runs a large number of simulation and store data for figures 4, 8-10;
8. tab3_multiprocessing.py runs a large number of rounds of simulation and store data for table3;
9. main.py is the main file that:
   1. generates data for figures based on single paths;
   2. generate graphs based on data from main_multiprocessing.py
   3. generate tables based on data from tab3_multiprocessing.py