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Computational Methods in Macro

The objective of the course is to learn how to solve the main workhorse models in macro using Python, with an emphasis on models with heterogeneous agents, and to show some leading applications of these models in recent literature. By the end of the course one should know the basic toolkits to solve and simulate macro models and should be able to apply/extend them for research purposes.

  1. Intro to python
  2. Numerical methods
  3. Representative agent models and methods
  4. Heterogeneous agents models and methods
  5. Heterogeneous agents + aggregate uncertainty models and methods
  6. Continuous time models and methods

This course is inspired by many great sources. Much of the content in this course is adapted from Kurt Mitman, Pontus Rendahl, Dirk Krueger, Jesus Fernandez-Villaverde, Per Krusell and Benjamin Moll's teaching materials. I deeply thank them for making these available.

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PhD level course on advanved macro models dealing with agent heterogeneity.

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