None apart from those requirements obligatory for entrance to the course (https://ls-eda.unibg.it/en).
In any case, it is strongly suggested to have attended the Advanced Econometrics course.
The course aims at introducing students to the main theoretical and empirical tools used for data analysis and applied empirical research. The course will focus on the identification and estimation of causal relationships. At the end of the course, participants should be able to critically read scientific articles and to start designing and performing their own analysis using the tools illustrated. The methods presented will allow students to address policy questions that are relevant from a social and economic perspective. For example: Does smoking cause cancer? Does an additional year of education increase future earnings? Does job training increase productivity? Does minimum wage affect unemployment? Does more police reduce crime rate?
Students will be introduced to applied micro econometrics toolkits and at the end of the course students will be able to search for policy relevant questions, define a credible and appropriate research design, collect data and perform empirical analysis through the use of econometric software (i.e. Stata).
The course covers empirical strategies for applied policy questions. The main objective of the course is to introduce students to the empirical tools for counterfactual analysis. The course will discuss and present the identification strategies and estimation techniques that are relevant for the identification of causal effects using observational data.
Specifically, the course will cover the following topics:
• Panel Data.
• The ideal experiment: Causal effects and the selection problem.
• Matching and Propensity Score.
• Randomized controlled trials.
• From observational data to natural experiments: instrumental variables.
• Panel, difference-in-differences, and synthetic control models.
• Regression discontinuity designs.
• Quantile regressions
The emphasis will be on the practical implementation of each approach using Stata.
The course combines traditional lectures with practical sessions during which the students will have the possibility to familiarize themselves with the techniques presented in the lectures. Both traditional lectures and practical sessions aim at fostering participation and class discussion.
The procedure and content of the exam will be the same for both attending and non-attending students. The final evaluation consists of:
- Research proposal (50% of the final grade). The purpose of this task is two-fold: 1) developing practical skills, 2) offering the students the opportunity to apply their knowledge to real data.
- Final written exam (50% of the final grade).
Most of the papers that will be discussed during classes are available through the UniBG On-line Library Services. Dataset, problem set, additional documents and papers will be posted on the eLearning website of the course.