Updated A.Y. 2022-2023
OVERVIEW AND PREREQUISITES
The aim of this course is to provide a survey of state-of-the-art microeconometric techniques. We will cover linear models for longitudinal data and a variety of causal inference designs and methods. We will discuss the strengths and weaknesses of these tools using economic applications.
A willingness to work hard on possibly unfamiliar material is key, and in addition to the material covered in the Mathematics (8011190), Statistics (8010848), and Econometrics (8011571) courses, I also expect that you are reasonably proficient with the Stata statistical software.
Grades will be based on a weighted average of the marks obtained in a final written exam (60%) and an empirical project (40%).
Empirical project: the topic and the research question must be chosen by each student, and groups with a maximum of 3 students are allowed. Each student/group will be responsible for communicating the topic of interest to the teacher, the data sources to be used, and the econometric technique (among those included in the course program) to be applied. All projects must be approved in advance. Each student/group will be responsible for a final presentation of the work (about 30 minutes). All students belonging to a certain group must be able to present the analysis carried out. The last 5 minutes will be reserved for comments/questions. Each group/student must make the presentation available to the rest of the class. For each student, the clarity of the presentation and the ability to answer any questions/comments will be assessed.
TEXTBOOKS AND MATERIAL
The main reference is Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
For further study in microeconometrics, I suggest Hansen (2021), Hsiao (2014), Cameron and Trivedi (2005), and Peracchi (2001). Angrist and Pischke (2009) and Cunningham (2021) for a more "applied" perspective.
Hansen B.E., (2020), Econometrics, download the book here.
Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Cameron C., Trivedi P.K., (2005), Microeconometrics: methods and applications, Cambridge University Press.
Hsiao C.,(2014), Analysis of Panel Data. Cambridge University Press, New York, NY, 3rd edition.
For a focus on applied issues, see:
Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press.
Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’ Companion, Princeton University Press, Princeton.
Lecture notes, slides, and Stata codes will be posted in the material section. Suggestions for further reading will be provided in class.