Updated A.Y. 2018-2019

Overview and requirements

The course provides a detailed overview of microeconometric methods for both cross-sectional and panel data. It focuses on nonlinear and advanced panel data models, with special emphasis on intuitions and applications. The relevant methodologies are discussed in class and then implemented during lab sessions, where students will have the opportunity to gain hands-on experience using the Stata statistical software. At the end of the course students should be able to specify the appropriate econometric model for investigating the problem under study and draw proper conclusions based on the results. This course is taught at a level assuming comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).



  • Cross-sectional Data Models for Discrete and Limited Dependent Variables
  1. Binary outcomes models
  2. Multinomial outcomes models
  3. Models for truncated/censored data and sample selection models
  4. Count data models
  • Panel Data Models
  1. ​Static linear panel data models: within group, first-difference, GLS and between group estimators
  2. Dynamic linear panel data models
  3. Nonlinear models: binary, truncated/censored and count outcomes
  4. (Optional: Spatial panel data models)
  • Treatment Evaluation
  1. ​Setup and main assumptions
  2. Treatment effects and selection bias
  3. Matching and propensity score estimators
  4. Differences-in-differences estimators
  5. Regression discontinuity design


References and material

  • Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
  • Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
  • Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).

The material, including lecture slides and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).