Lecture Improvement Project

[HumphreyYang]

When running through the list, please follow the following workflow:

1. First, read each lecture carefully and check for typos and mathos (mathematical errors). If a derivation has a gap, consider adding an exercise to help fill it.

2. Rewrite the code in JAX where beneficial or feasible. One useful criterion is to check whether there are extensive loops or if the code is accelerated with numba.

3. Submit one pull request (PR) per lecture and tag @mmcky and @HumphreyYang for review.

4. Note any questions or concerns in the PR. We’ll gather these into a bi-weekly summary/news feed for the group.

5. Synchronize updates to the Chinese version.

Intermediate Series

Tools and Techniques

• sir_model
• linear_algebra
• qr_decomp
• eig_circulant
• svd_intro
• var_dmd (there is no simulation in this one)
• newton_method (There is a JAX version here).

Elementary Statistics

• prob_matrix
• stats_examples
• lln_clt
• prob_meaning
• multi_hyper
• multivariate_normal
• hoist_failure (code needs to be cleaned up)
• back_prop (already in JAX -- it has some overlaps with @jstac's material on AI and economics and this lecture. We can try to integrate them into one)
• rand_resp
• util_rand_resp

Bayes Law

Note for this section, there are discrepencies in terms of tools we used. Consider unifying them and write a introductory section for numpyro in bayes_nonconj?)

• bayes_nonconj (used both pyro with torch backend and numpyro with JAX backend)
• ar1_bayes (used pymc and numpyro)
• ar1_turningpts (used pymc but not numpyro)

Statistics and Information

• likelihood_ratio_process
• imp_sample
• wald_friedman
• wald_friedman_2
• exchangeable
• likelihood_bayes
• mix_model
• navy_captain

Linear Programming

• opt_transport
• von_neumann_model

Introduction to Dynamics

• finite_markov
• inventory_dynamics (There is a JAX version here)
• linear_models
• samuelson
• kesten_processes (There is a JAX version here)
• wealth_dynamics (There is a JAX version here)
• kalman
• kalman_2

Search

• mccall_model
• mccall_model_with_separation
• mccall_fitted_vfi
• mccall_correlated
• career
• jv
• mccall_q
• odu

Consumption, Savings and Capital

• cass_koopmans_1
• cass_koopmans_2
• cass_fiscal
• cass_fiscal_2
• ak2
• cake_eatingproblem
• cake_eating_numerical
• optgrowth
• optgrowth_fast
• coleman_policy_iter
• egm_policy_iter (egm is implemented in JAX here)
• ifp
• ifp_advanced

LQ Control

• lqcontrol
• lagrangian_lqdp
• cross_product_trick
• perm_income
• perm_income_cons
• lq_inventories

Multiple Agent Models

• lake_model
• rational_expectations
• re_with_feedback
• markov_perf
• uncertainty_traps
• aiyagari (There is a JAX version here)

Asset Pricing and Finance

• markov_asset (There is a JAX version here)
• ge_arrow
• harrison_kreps

Data and Empirics

• pandas_panel
• ols (There is a related JAX lecture here)
• mle (There is a JAX version here)

Auctions

• two_auctions
• house_auction

Advanced Series

Tools and Techniques

• orth_proj
• stationary_densities
• muth_kalman
• discrete_dp

LQ Control

• cons_news
• smoothing
• smoothing_tax
• markov_jump_lq
• tax_smoothing_1
• tax_smoothing_2
• tax_smoothing_3
• lqramsey

Multiple Agent Models

• arellano (There is a JAX version here)
• matsuyama
• coase
• match_transport

Dynamic Linear Economies

• hs_recursive_models
• growth_in_dles
• lucas_asset_pricing_dles
• irfs_in_hall_model
• permanent_income_dles
• rosen_schooling_model
• cattle_cycles
• hs_invertibility_example

Risk, Model Uncertainty, and Robustness

• five_preferences
• entropy
• robustness
• rob_markov_perf

Time Series Models

• arma
• estspec
• additive_functionals
• lu_tricks
• classical_filtering
• knowing_forecasts_of_others

Asset Pricing and Finance

• lucas_model (There is a JAX version here)
• asset_pricing_lph
• black_litterman
• BCG_complete_mkts
• BCG_incomplete_mkts

Dynamic Programming Squared

• un_insure
• dyn_stack
• calvo_machine_learn
• calvo
• calvo_abreu
• opt_tax_recur
• amss
• amss2
• amss3
• chang_ramsey
• chang_credible

[jstac]

Thanks @HumphreyYang ! Great summary.

I learned a new word -- mathos :-)

All help is appreciated. If a lecture is not computationally intensive and it's written in numpy then we may as well keep it that way. As @HumphreyYang says, any lecture that's computationally intensive or accelerated by numba need to be considered as candidates for shifting to JAX. And all lectures will benefit from review.