Course 2024-2025 a.y.

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 (randomized control trials, instrumental variables, regression discontinuity and difference-in-differences).

• 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.

• Practical Exercises
• Collaborative Works / Assignments

• Exercises (Exercises, database, software etc.):  students have to solve 3 problem sets in groups. Problem sets consist on applying the methods studied in class to the analysis of real world data (datasets are provided) using statistical softwares (Stata or R). The tutor of the class helps students learn advanced features of these 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.

The final grade is a weighted average of: (i) three problem sets (in group) - weight=30% (ii) Final Exam - weight=70%.
 

The problem sets assess whether students learned how to implement microeconometrics tools (taught during lectures) in practice using a statistical software.

The final exam is composed of both open and closed questions.

In the first part, open questions assess whether students learned how to select the most relevant microeconometrics methods depending on the economics question to be studied and on the data availability and setting at hand. This part assess students critical thinking of the strength of empirical evidence through discussion of methodological assumptions and implementation challenges. 

In the second part, closed questions assess whether students learned the theory underlying the microeconometrics tools seen during the course, and whether they learned the typical practial issues associated with their use and implementation.  

The final grade is the grade obtained at the final exam.

The final exam is composed of both open and closed questions.

In the first part, open questions assess whether students learned how to select the most relevant microeconometrics methods depending on the economics question to be studied and on the data availability and setting at hand. This part assess students critical thinking of the strength of empirical evidence through discussion of methodological assumptions and implementation challenges. 

In the second part, closed questions assess whether students learned the theory underlying the microeconometrics tools seen during the course, and whether they learned the typical practial issues associated with their use and implementation.  

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

• We discuss material from the following textbooks (Most of the material in Angrist and Pischke is relevant for the course, but not compulsory. We cover only selected chapters of Imbens and Rubin, of Cameron and Trievdi, and of Woolridge).

• 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.