This comprehensive course aims to provide students with a strong foundation in data analysis using Stata. The course covers various tools and techniques for conducting descriptive statistics and advanced causal inference analysis. Throughout the lectures, students will acquire proficiency in essential Stata functionalities, including data management, data visualization, hypothesis testing, regression analysis, and causal inference methods. Upon completing the course, students will possess the skills necessary to address real-world data challenges with confidence, enabling them to make informed, data-driven decisions characterized by precision and analytical rigor. The course will also cover the basics of Latex, since this is a valuable tool to present data-analyses.
Metodi didattici
Each course session will revolve around the in-depth analysis of a selected research paper, which will serve as a focal point for our discussions and exercises. Students must read the designated paper in advance, enabling active engagement in both paper-related discussions and the in-class activities. While some exercises will directly relate to the selected paper, others will be supplementary, designed to reinforce broader concepts and skills. Active participation and critical thinking are highly encouraged throughout the course to foster a dynamic and collaborative learning environment.
Verifica Apprendimento
End of course assignment
Testi
Cunningham, Scott. Causal inference: The mixtape. Yale university press, 2021
Contenuti
1. Revision: regression and the omitted variable problem 2. Potential outcomes causal model and randomized control trials 3. Matching 4. Instrumental Variables 5. Regression Discontinuity Design 6. Panel Data 7. Difference-in-Differences
