The purpose of this module is to equip students with the specialist tools to conduct quantitative economic assessments relevant to competition cases and regulatory matters. It will cover key techniques to empirically analyze markets. We will use data-driven examples from selected industries in France and internationally. The content of the course will be based on a discussion of selected academic papers to introduce the theory and their practical applications to competition and regulatory cases.

The course will combine lectures and hands-on sessions during which real industry data sets will be used to conduct econometric estimations in R. Regular homework assignments will also enable to practice independently the empirical methods taught during the lectures.



Students should be able to:

  • Learn about the practical considerations to apply techniques, based on the lessons from various recent cases where quantitative techniques have been applied.
  • Understand the different techniques for quantitative assessment of competition and regulatory matters, such as in defining markets
  • Estimate demand functions (homogenous and differentiated products)
  • Undertake quantitative analysis relevant to analyzing competition and regulation matters such as the identification of market power, merger simulations, and estimation of damages.
  • Learn to interpret and critically evaluate the empirical results from different approaches.
  • Develop a good common sense of the advantages and disadvantages of different approaches, and the circumstances under which they are (not) suitable.
  • Acquire a thorough understanding of the data requirements for applying various techniques



  • Davis, P. and E. Garces (2009) “Quantitative Techniques for Competition and Antitrust Analysis”, Princeton University Press.
  • Luis Cabral (2000) “Introduction to Industrial Organization”, The MIT Press
  • Train, K. (2009) “Discrete Choice Methods with Simulation”, Cambridge University Press



  • Homework assignments (x3): 30%
  • Exam: 70%