MICROECONOMETRICS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES: The aim of this course is to deepen the topics covered in the econometrics course by introducing the student to the state-of-the-art techniques for estimating causal effects for both cross-sectional and longitudinal data.
KNOWLEDGE AND UNDERSTANDING: Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.
APPLYING KNOWLEDGE AND UNDERSTANDING: Based on the analytical tools and the knowledge acquired through theoretical and practice sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R. In particular, they will need to be able to correctly specify a regression model, choose the most appropriate estimation approach and correctly interpret the empirical results.
MAKING JUDGEMENTS: Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.
COMMUNICATION SKILLS: Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.
LEARNING SKILLS: Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.
KNOWLEDGE AND UNDERSTANDING: Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.
APPLYING KNOWLEDGE AND UNDERSTANDING: Based on the analytical tools and the knowledge acquired through theoretical and practice sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R. In particular, they will need to be able to correctly specify a regression model, choose the most appropriate estimation approach and correctly interpret the empirical results.
MAKING JUDGEMENTS: Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.
COMMUNICATION SKILLS: Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.
LEARNING SKILLS: Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.
Prerequisites
Students should have completed Mathematics (8011190), Statistics (8010848) and Econometrics (8011571). A good knowledge of Stata and/or R is required.
Program
Limited dependent variable models
Binary and multinomial outcomes
Count data outcomes
Causal inference
Potential outcomes perspective
Treatment effects and selection bias
Main assumptions and estimands
Regression, matching and propensity score estimators
Regression discontinuity design
Panel data
Difference-in-differences
(Generalized) Synthetic controls
Binary and multinomial outcomes
Count data outcomes
Causal inference
Potential outcomes perspective
Treatment effects and selection bias
Main assumptions and estimands
Regression, matching and propensity score estimators
Regression discontinuity design
Panel data
Difference-in-differences
(Generalized) Synthetic controls
Books
The main reference is Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:
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.
For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:
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.
Bibliography
Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).
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.
Teaching notes and suggestions for further reading will be provided in class.
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.
Teaching notes and suggestions for further reading will be provided in class.
Teaching methods
The course consists of eighteen theoretical lectures, with applications based on economic micro-data.
Exam Rules
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, groups with a maximum of 3 students are allowed. Each student / group will be responsible for communicating to the teacher the topic of interest, 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 presentation and the ability to answer any questions / comments will be assessed.
Empirical project: the topic and the research question must be chosen by each student, groups with a maximum of 3 students are allowed. Each student / group will be responsible for communicating to the teacher the topic of interest, 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 presentation and the ability to answer any questions / comments will be assessed.