Graduate Teaching

Computational Economics

The goal of this course is help students make the transition from formal Econometrics and Theory training to applications. We will cover methods used to analyze numerically the predictions of micro-economic economic models, and estimate technology and preference parameters. The syllabus is available here.

Lecture slides:

  1. Intro: Maximum likelihood estimation of discrete-choice models
  2. Multinomial-choice models and simulation methods
  3. Aggregate models of demand for differentiated products
  4. Estimation of dynamic discrete choice models
  5. Dynamic games and industry dynamics

Problem Sets:

  1. Maximum likelihood estimation
  2. Numerical integration
  3. Nonlinear GMM and nested-fixed-point
  4. Estimation of dynamic discrete choice models
  5. Markov Perfect Equilibrium


Graduate Empirical Industrial Organization

I taught different versions of “Empirical IO” over the years. Below you will find lecture slides organized by topics. Randomly updated. Here is a the most recent syllabus.

  1. Demand for differentiated products
  2. Market power:
  3. Vertical contracting
  4. Search and matching frictions
  5. Dynamic discrete choice
  6. Entry and product positioning
  7. Productivity and industry turnover
  8. Markov Games: