Global Style Tracker

[arnavs]

This is a meta-issue focused on enumerating the global stylistic changes to the lectures. As before, we can check off each seciton when we are complete.

• [ ] Remove docstrings
• [ ] Convert to using Parameters and unpack throughout and all default constructors to @with_kw #135
• [ ] Replacing homebrewed quadrature with Expectations.jl #133
• [ ] Replacing homebrewed iteration and EQ ones with with fixedpoint() #134
• [ ] Eliminating repeatedly nested loops (like in the career and job choice lecture).
• [ ] Using Plots/GR globally #67
• [ ] Convert to Maximize and simplify sign flips #158
• [ ] Convert to using Parameters and unpack throughout #135
• [ ] Use interpolations #146
• [ ] Global removal of virtually all type signatures

[arnavs]

Also: eliminate double transposes wherever they occur (such as in the Kalman filter lecture).

Edit:

• Seed random numbers (with 42, preferably) whenever possible.

[jlperla]

Active template for checklists:

Getting Started with Julia

• [ ] Setting up your Julia Environment
• [ ] An Introductory Example
• [ ] Julia Essentials and more material
• [ ] Fundamental Types with more material
• [ ] Introduction to Types and Generic Programming

More Programming and Software Engineering Principles

• [ ] Generic Programming
• [ ] General Purpose Packages
• [ ] Data and Statistical Ecosystem
• [ ] Solvers, Optimizers, AD
• [ ] GitHub and Version Control
• [ ] Tools and Editors
• [ ] Testing and CI

Tools and Techniques

• [ ] Linear Algebra
• [ ] Orthogonal Projections and their Applications
• [ ] LLN and CLT
• [ ] Linear State-Space Models
• [ ] Finite Markov Chains
• [ ] Continuous State Markov Chains
• [ ] A First Look at the Kalman Filter

Dynamic Programming

• [ ] Shortest Paths
• [ ] Job Search I: The McCall Search Model
• [ ] Job Search II: Search and Separation
• [ ] A Problem that Stumped Milton Friedman
• [ ] Job Search III: Search with Learning
• [ ] Job Search IV: Modeling Career Choice
• [ ] Job Search V: On-the-Job Search
• [ ] Optimal Growth I: The Stochastic Optimal Growth Model
• [ ] Optimal Growth II: Time Iteration
• [ ] Optimal Growth III: The Endogenous Grid Method
• [ ] LQ Dynamic Programming Problems
• [ ] Optimal Savings I: The Permanent Income Model
• [ ] Optimal Savings II: LQ Techniques
• [ ] Consumption and Tax Smoothing with Complete and Incomplete Markets
• [ ] Optimal Savings III: Occasionally Binding Constraints
• [ ] Robustness
• [ ] Discrete State Dynamic Programming

Multiple Agent Models

• [ ] Schelling's Segregation Model
• [ ] A Lake Model of Employment and Unemployment
• [ ] Rational Expectations Equilibrium
• [ ] Markov Perfect Equilibrium
• [ ] Asset Pricing I: Finite State Models
• [ ] Asset Pricing II: The Lucas Asset Pricing Model
• [ ] Asset Pricing III: Incomplete Markets
• [ ] Uncertainty Traps
• [ ] The Aiyagari Model
• [ ] Default Risk and Income Fluctuations
• [ ] Globalization and Cycles

Time Series Models

• [ ] Covariance Stationary Processes
• [ ] Estimation of Spectra
• [ ] Additive Functionals
• [ ] Multiplicative Functionals
• [ ] Classical Control with Linear Algebra
• [ ] Classical Filtering with Linear Algebra

Dynamic Programming Squared

• [ ] Dynamic Stackelberg Problems
• [ ] Optimal Taxation in an LQ Economy
• [ ] Optimal Taxation with State-Contingent Debt
• [ ] Optimal Taxation without State-Contingent Debt