EMPIRICAL ECONOMICS

In this course we study the methods for causal inference. Causal inference focuses on uncovering, i.e. learning about, causal relationships: the scientist is interested in quantifying the effect of a cause (also called a treatment) on one or more outcome variables of interest. Methods of causal inference are widely used both in academia and industry to ex-post assess the effect of policy interventions, where the term policy is broadly understood to include any intervention of interest by public or private agents. The 2021 Nobel price was awarded to Joshua Angrist, Guido Imbens, and David Card for their contributions to the analysis of causal relationships and empirical labor economics. In this course we learn some of the inferential methods that they pioneered. The methods covered are: matching, instrumental variables and local average treatment effect (LATE) and regression discontinuity design.

1. Knowledge and understanding
- know experimental and quasi experimental econometric methods for the causal analysis
- able to understand and interpret the result of complex microeconometric analysis
- Knowledge of the necessary Stata code to run a causal inference analysis

2. Ability to apply knowledge and understanding
- able to develop an empirical strategy for causal identification and to select and apply the appropriate econometric method for a specific research question.
- Able to carry on in Stata an econometric analysis to identity a causal relation

4. Communication skills
- able to present in a clear and precise way the result of a causal analysis

5. Learning skills
- Have the tools to deepen the knowledge of more advanced and more specific econometric analysis for causal identification

This is a second year master course in econometrics. The instructor will give for granted the content of Probability and Statistics, Econometrics I, microeconomics I and II

graphical causal modeling
Rubin Causal Model and Randomized Control Trials
Matching
Regression Discontinuity
Instrumental Variables

Scott Cunningham (2021), Causal Inference: The Mixtape, Yale University Press.
The book is available online and for free at http://mixtape.scunning.com/ .
A selection of papers posted on moodle
A few chapters from other books that will be specified during the course

Take home exam. At the end of the course an exam will be assigned which may include both theoretical questions and analyzes to be carried out in Stata. The exam must be carried out by each student individually at home

Standard calsses and practical sessions on Stata

English

written

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development