Michael Cai
Economics PhD Student at Northwestern University
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolMichael Cai is an Economics PhD student at Northwestern with a decade of quantitative experience combining macroeconomic research and production-grade code contributions. He has practical experience at the New York Fed DSGE team and has implemented and tested Monte Carlo algorithms for DSGE estimation in Julia, improving posterior tempering and SMC structure. His open-source work reorganizing core modules for heterogeneous-agent macro frameworks shows strength in backend design, numerical methods, and reproducible research workflows. Based in New York and summa cum laude from NYU Stern, he blends rigorous theory with hands-on computational implementation to study policies that support equitable growth. An often-overlooked strength is his ability to bridge research-grade model development with software engineering practices that make complex macro models testable and extensible.
10 years of coding experience
Bachelor of Science (B.S.), Economics, summa cum laude, Bachelor of Science (B.S.), Economics, summa cum laude at New York University - Leonard N. Stern School of Business
Github Skills (13)
model-building10
statistics10
bayesian10
statistical-models10
python10
statistic10
scientific-computing10
bayesian-inference10
modeling10
model-driven-development10
model-driven10
numpy9
linear-algebra7
Programming languages (5)
Github contributions (5)
shade-econ/sequence-jacobian
Jul 2020 - Nov 2021
A unified framework to solve and analyze heterogeneous-agent macro models.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:305 commits, 2 PRs, 42 pushes in 1 year 4 months
Contributions summary:Michael reorganized the code into folders, set relative source code load paths, and initialized modules. Their work involved changes to the `het_block.py`, `solved_block.py`, `asymptotic.py`, `nonlinear.py`, `utils.py`, `models/hank.py`, and other model files and notebooks which suggests the user focused on the core logic, data structures and underlying systems of the codebase, specifically with modules of the `sequence-jacobian` package. The user made changes to the internal block construction and organization and also incorporated a few new features.
solveagentheterogeneousmacroanalyze
FRBNY-DSGE/DSGE.jl
Jun 2016 - Sep 2019
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Role in this project:Data Scientist
Data ScientistContributions:4 releases, 1072 commits, 1 PR in 3 years 3 months
Contributions summary:Michael implemented and tested a single step of the RWMH algorithm within the provided code repository, which is used for solving and estimating Dynamic Stochastic General Equilibrium models. The user created a test file, and developed a function to test the proposed mutations. The user also made adjustments to the posterior function for tempering and improved the structure of the smc code.
solveequilibriumdynamical-systemsbayesian-inferencedsge
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