Course 2024-2025 a.y.

20295 - MICROECONOMETRICS

Department of Economics

Course taught in English

Student consultation hours
Class timetable
Exam timetable
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/05) - M (6 credits - II sem. - OP  |  SECS-P/05) - IM (6 credits - II sem. - OP  |  12 credits SECS-P/05) - MM (6 credits - II sem. - OP  |  SECS-P/05) - AFC (6 credits - II sem. - OP  |  SECS-P/05) - CLELI (6 credits - II sem. - OP  |  SECS-P/05) - ACME (6 credits - II sem. - OP  |  SECS-P/05) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/05) - EMIT (6 credits - II sem. - OP  |  SECS-P/05) - GIO (6 credits - II sem. - OP  |  SECS-P/05) - DSBA (6 credits - II sem. - OP  |  SECS-P/05) - PPA (6 credits - II sem. - OP  |  SECS-P/05) - FIN (6 credits - II sem. - OP  |  SECS-P/05) - AI (6 credits - II sem. - OP  |  SECS-P/05)
Course Director:
THOMAS EMILE ROBERT LE BARBANCHON

Classes: 31 (II sem.)
Instructors:
Class 31: THOMAS EMILE ROBERT LE BARBANCHON


Suggested background knowledge

A basic knowledge of econometrics is strongly recommended. Basic knowledge on the use of STATA or similar computer software is also recommended.

Mission & Content Summary

MISSION

This course covers theoretical and empirical developments on Microeconometrics, with a focus on program evaluation. It provides students with basic skills to conduct rigorous estimation of the impact of governmental/aid agencies programs. Moreover, it conveys the theoretical background to test implications or assumptions of microeconomic models and to understand empirical applications in several applied fields, such as development, labor, political economy, health and education. The course has an applied focus, but theoretical material is broadly covered. Estimation techniques and econometric theory are discussed during lecture; with each method being motivated by a series of empirical papers. The problem sets focus on helping students understand how these methods can be applied to real world data.

CONTENT SUMMARY

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).


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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.

Teaching methods

  • Practical Exercises
  • Collaborative Works / Assignments

DETAILS

  • Exercises (Exercises, database, software etc.):  students have to solve 4 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 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.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Collaborative Works / Assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING STUDENTS

The final grade is a weighted average of: (i) four problem sets (in group) - weight=40% (ii) Final Exam - weight=60%.
 


NOT ATTENDING STUDENTS

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


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • 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. 
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