TOPICS IN COMPUTATIONAL ECONOMICS

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Apply numerical methods to solve dynamic programming problems (including parallel computing) Audience: Graduate 2. Solve and simulate dynamic equilibrium models with heterogenous agents to understand such issues as wealth inequality and the the firm size distribution Audience: Graduate 3. Use simulated method of moments to calibrate dynamic models with heterogeneous agents Audience: Graduate 4. Solve and estimate static and dynamic discrete choice models to understand such issues as demand for differentiated products, residential sorting, investment decisions Audience: Graduate 5. Solve dynamic Markov games with strategic interactions to understand such issues as strategic investments and the evolution of the firm size distribution Audience: Graduate Learning Outcomes: 1. Apply numerical methods to solve dynamic programming problems (including parallel computing) Audience: Graduate 2. Solve and simulate dynamic equilibrium models with heterogenous agents to understand such issues as wealth inequality and the the firm size distribution Audience: Graduate 3. Use simulated method of moments to calibrate dynamic models with heterogeneous agents Audience: Graduate 4. Solve and estimate static and dynamic discrete choice models to understand such issues as demand for differentiated products, residential sorting, investment decisions Audience: Graduate 5. Solve dynamic Markov games with strategic interactions to understand such issues as strategic investments and the evolution of the firm size distribution Audience: Graduate