The primary objective of this course is to ensure that students acquire and effectively apply essential computational and statistical tools to study the quantitative implications of a structural model of their choice. The course is based on classical computational techniques in macroeconomics (e.g., dynamic programming, perturbation, projection methods, simulation-based methods etc.), recent advancements in machine learning (deep learning and reinforcement learning), and high-performance computing (parallel computing, MPI, GPU, etc.). This serves as a catalyst for PhD students to make significant strides in the quantitative aspects of their dissertations.

This course will be valuable for individuals working in quantitative macroeconomic analysis and those focusing on structural microeconomic models. The primary programming languages utilized in the course will be Python and PyTorch.