The primary objective of this course is to equip students with the computational and statistical tools necessary to analyze and quantify the implications of structural economic models of their choice. The course integrates foundational computational techniques in macroeconomics (e.g., dynamic programming, perturbation, projection methods, and simulation-based methods) with recent advancements in artificial intelligence and machine learning. A key focus is on the application of technologies such as deep learning, reinforcement learning, and their role in solving macroeconomic models.

Additionally, the course will explore other pivotal AI technologies, including natural language processing and large language models. Students will learn how these tools work, examine their applications in economics, and engage with the broader implications of their use in economic research. Given the rapidly evolving nature of AI and its applications, discussions on these topics will be adaptive and may update/expand to reflect advancements in the field.

Designed to serve as a catalyst for faculty and graduate students, the course aims to enhance their ability to tackle quantitative aspects of their research and contribute meaningfully to cutting-edge research. It is particularly valuable for individuals pursuing quantitative macroeconomic analysis or structural microeconomic modeling.

The course also emphasizes high-performance computing technologies (e.g., parallel computing, MPI, and GPU programming) to enable efficient problem-solving. Practical implementation is a central focus, with Python and PyTorch as the primary programming languages, preparing students to solve complex economic problems effectively and efficiently.

• Numerical Solution Methods for Dynamic Models
  • Solving the canonical optimal growth model using state-space discretization.
  • Writing and implementing the first code to solve a macro model.
  • Assessing accuracy, error bounds, and convergence of numerical algorithms.
• Advanced Numerical Methods
  • Improvements over basic methods:
    • Howard’s improvement.
    • Exploiting monotonicity and concavity.
  • Introduction to the finite element method for solving dynamic models.