Resources
A compact collection of papers, slides, and code notes that I use in computational economics and neural methods for dynamic models.
Presentations: AI Methods for Solving HAMs
Code
Replication: Deep Learning for Solving Dynamic Economic Models
Krusell-Smith model replication materials.
Replication: Deep Equilibrium NETs
Benchmark implementation for DEQN-style methods.
Replication: Estimating Nonlinear Heterogeneous Agent Models with Neural Networks
RANK + ZLB replication materials.
High-Dimensional Dynamic Programming with PyTorch
Deep learning implementation for high-dimensional dynamic programming problems.
Aiyagari Model with Transitions
Code notes for transition dynamics in an Aiyagari environment.
CUDA Parallel Aiyagari Solver
CUDA implementation and performance comparison against Matlab and Fortran baselines.