PhD Computing

This is the second core course for first-year economics PhD students in methods taught at the University of Florida. 

Part one covers the basics of computational methods and setup, including GitHub, IDE, language, math tools, and basic distribution and optimization methods. Part two covers more advanced optimization methods, iterations, applications, and approximation. Part three covers advanced topics, including heterogeneous agents, structural estimation, parallel computing, and some machine learning methods. 

Syllabus:  2025 Spring

Lecture Slides: GitHub Link

Textbooks:

Thomas J. Sargent and John Stachurski, Dynamic Programming

Kenneth L. Judd, Numerical Methods in Economics

Jianjun Miao, Economic Dynamics in Discrete Time

Jesús Fernández-Villaverde, Machine Learning for Economists (Slides)

Topics:

Part I: Basic Topics

1. Beyond Economics: How to code like a Software Development Engineer: GitHub, IDE, Config, etc...

2. Language Choices: Fast, easy, scalable, or just as long as it works

3. Mathematical Tools: How to turn equations into code

4. MATLAB/Python/Julia Programming for Economics and Finance

5. Linear Dynamics, Probability and Distributions, Nonlinear Dynamics

6. Stochastic Dynamics, Optimization, Estimation (Basics)

7. Optimization Methods (Gradient or Gradient-Free)

8. Value Function Iteration, Policy Function Iteration, Endogenous Grid Method

Part II: Advanced

1. Heterogeneous Agent Models without Aggregate Uncertainty

2. Heterogeneous Agent Models with Aggregate Uncertainty

3. From Heterogeneous Households to Heterogeneous Firms

4. Structural Estimation with Simulated Method of Moments

5. Advanced Methods for Heterogeneous Agent Models with Aggregate Uncertainty

6. Machine Learning for Economists (Touch Base)