jstac / jitting_py_lectures Goto Github PK

I think that it makes more sense for compute_fixed_point to take an operator ie. T as its main argument, rather than the object

Copy of QuantEcon/lecture-source-py#35

A list of lectures on dynamic programming and closely related topics we would like to jit in a similar way

• Job Search I: The McCall Search Model (previously jitted in a simple style, probably OK as is) @natashawatkins
• Job Search II: Search and Separation (previously jitted but needs to be put in same style as others) @natashawatkins
• A Problem that Stumped Milton Friedman @QBatista
• Job Search III: Search with Learning @natashawatkins
• Job Search IV: Modeling Career Choice #5 @natashawatkins
• Job Search V: On-the-Job Search @natashawatkins
• Optimal Growth I: The Stochastic Optimal Growth Model #4 @natashawatkins
• Optimal Growth II: Time Iteration @rebekahanne
• Optimal Growth III: The Endogenous Grid Method @bkaplowitz
• LQ Dynamic Programming Problems - nothing to be jitted in this lecture as all the code uses functions from QuantEcon.py
• Optimal Savings I: The Permanent Income Model @natashawatkins
• Optimal Savings II: LQ Techniques - nothing to be jitted in this lecture as all the code uses functions from QuantEcon.py
• Consumption and Tax Smoothing with Complete and Incomplete Markets @rebekahanne
• Optimal Savings III: Occasionally Binding Constraints @shizejin
• Robustness - nothing to be jitted in this lecture as all the code uses functions from QuantEcon.py
• Asset Pricing II: The Lucas Asset Pricing Model @cc7768

Somewhat different structure but we still want to jit

• The Aiyagari Model (substantial changes required --- use code from occasionally binding constraints lect)
• Continuous State Markov Chains
• Default Risk and Income Fluctuations (jitted but needs to be put in the same style as others)