The goal of this seminar is to bridge the gaps between Econometrics and Machine Learning, and to explore new avenues of research that can crossbreed these two approaches in a productive way.
The development of machine learning methods and the use of big data have been exploding over the past decade, and sometimes presented as an alternative to more traditional approaches in econometrics that have been (and still are) widely used in the social sciences. Historically, the two fields have been competing. However, the recent literature has emphasized that they are complementary and have strong synergies. Machine learning can leverage econometrics to design new methods with improved flexibility to address new complex problems. Moreover, the collection of Big Data creates new challenges for traditional econometric methods in terms of computation and identification. Machine learning may allow researchers to tackle some of these challenges.
The overall purpose of the course is to provide a fundamental understanding of microeconometric methods and of their applications. It gives a detailed overview of the available estimation methods, from the classical approach to the Bayesian approach, providing both the theoretical foundations of these methods, as well as practical tools to implement them empirically. Microeconomic models that are widely used in practice to model individual behaviors and decisions will be introduced and used to illustrate how the various estimation methods can be applied.
Econometrics C is the final course in the compulsory BSc. course sequence in Statistics and Econometrics. The course Econometrics B focuses on linear regression and instrumental variables estimation of the linear regression model for cross–sectional data. The current course goes into more details with the estimation principles and presents the generalized method of moments and the likelihood analysis. Econometrics C also discusses dependent observations and gives a detailed account of the econometric analysis of time series data. As an integral part of the course, students are introduced to statistical tools for analyzing time series and panel data.