The main objective is to learn how to apply econometrics techniques studied so far to inform managerial decisions, from investment selection by companies to the selection of public policies by the state. These skills are an essential part of economists' toolbox, and very useful for both public and private employers.
The course will cover the following topics:
- Using econometrics to inform decisions
- Causal inference: Counterfactual outcomes and treatment effects
- Selection bias and randomization as the gold standard
- Regression as a model for conditional averages
- Multiple regression: control variables
- Non-linear models: log-transformation and polynomial specifications
- Dummy variables and interaction terms
- Models for binary (or categorical) outcomes
- Repeated observations and panel data models
- Introduction to causal inference: Difference-in-differences
Each week we will hold one session in the classroom and one session in the computer lab. In the classroom we will introduce concepts and review a number of important econometric tools for data analysis. In the lab we will implement these tools with data and build up software skills. By the end of the class, students will have acquired skills to manipulate data in Excel (matching datasets, pivot tables) and use Stata to run regressions and prepare results for a publication. Students will also get to read high-quality peer-reviewed articles on different topics in economics to understand how empirical results are presented and interpreted.
Important note: The language for the course is English, and this for two reasons. First, you need to build up the relevant vocabulary to read and interpret the scientific literature (which is almost exclusively in English). Second, this class prepares you for good master programs and job opportunities in international environments, where a good command of English is usually a pre-requisite.
Dans le cadre de ce cours, nous passons une ou deux séances sur l’estimation des différences salariales hommes/femmes. Durant ces séances, les étudiants utilisent des données pour essayer de quantifier cette différence et interpréter les résultats.