Insegnamento a.a. 2020-2021

• The course begins with a discussion about reduced form and structural form analysis, the counterfactual notion of causality and the differences between estimation and identification.
• The main methodological part is devoted to the estimation of causal relationships, including experimental and non-experimental techniques (matching, instrumental variables, regression discontinuity and panel data).

• Differentiate the notions of causality and correlation, identification and estimation.
• Understand the assumptions behind the different impact evaluation methodologies.
• Decide what empirical strategy to use when evaluating a program.
• Learn how to recover causal relationships using experimental and non-experimental techniques.

• Be able to read empirical papers trying to estimate causal effects.
• Use impact evaluation techniques to solve problems with real world data.
• Conduct or help conduct rigorous estimation of the imapct of governmental/aid agencies programs.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Case studies /Incidents (traditional, online)
• Group assignments

• Exercises (Exercises, database, software etc.):  students have to solve 4 problem sets in groups of 5. Problem sets consist on applying the methods studied in class to the analysis of real world data (datasets are provided) using the statistical software Stata. The tutor of the class helps students learn advanced features of this statistical software.
• Case Studies: after the discussion of the theory behind each methodology, we cover an empirical paper using that methodology to answer relevant policy questions.
• Group Assignments: students present one recent working paper that covers the methods studied in class, and all the class discusses the assumptions and contributions.

Final Exam (50%)

Problem sets (40%):

• 4 problem sets.
• All of them must be submitted.
• Can be prepared and submitted in groups of 5 students.

Presentation (10%):

• One presentation in Groups of 5 students. Students can choose one of the applied topics (Experiments, Matching, IV, RDD, DID, Standard Errors) to present a paper of their choice that uses the discussed methodology.

• The main compulsory material is based on Lecture slides provided for each topic and posted online.
• For each topic, a long list of recently published papers is provided (all of them are optional)

•  We discuss material from the following textbooks (Most of the material in AP is relevant for the course, but not compulsory. We cover only selected chapters of IR, CT and W).

• J. ANGRIST, J. PISCHKE, Mostly Harmless Econometrics, Princeton University Press. [AP], 2009.
• G. IMBENSI, D. RUBIN,  Causal Inference for Statistics, Social and Biomedical Sciences. An Introduction, University Press. [IR], 2015.
• CAMERON, A. COLIN, K. PRAVIN, TRIVEDI, Microeconometrics. Methods and Applications, Cambridge University Press, New York. [CT], 2005. 
• CAMERON, A. COLIN, K. PRAVIN, TRIVEDI, Microeconometrics Using Stata, Stata Press, 2009.
• J. WOOLDRIGE,  Econometric Analysis of Cross Section and Panel Data, MIT Press, 2nd Edition. [W], 2010.