Data-driven economic analysis

The aim of this course is twofold. The first aim is to explain how economists take their theoretical models to the data. In particular, the course presents a set of basic economic models for the analysis of individual behaviour and market and non-market transactions, and illustrates which data are available to translate theoretical predictions into empirically testable research questions. The second aim is to analyse the main challenges faced by data scientists in answering empirical questions rooted in economic theory using data from standard and non-standard sources. The main emphasis will be on learning how to establish causal relationships between variables and how to exploit machine learning techniques to inform policy makers' decisions.

Upon completion of the course students will be able to:
1. understand basic economic models and data sources.
2. understand the issues involved in causal inference in the field of economics.
3. carry out regression analyses in Stata and interpret results.
4. apply basic machine learning techniques to assist causal inference.