The BeNA Skills Camp is a format aiming at doctoral students in economics, who want to enhance their research skills.
Suggestions for future skills camps can be send to firstname.lastname@example.org.
The third skill camp was Machine Learning Methods for Prediction and Treatment Effect Estimation offered by Marica Valente and took place onfrom 10-12 June, 2020 via ZOOM.
The second skills camp was Machine Learning: An Applied Econometric Approach offered by Jann Spiess, Assistant Professor from Stanford Graduate School of Business. The camp was organised jointly by BeNA and the Berlin School of Economics (BSE) with the support of Collaborative Research Center TRR 190 Rationality and Competition and took place from 4-6 September, 2019.
Course description: Machine learning has created many engineering break-throughs from real-time voice recognition to automatic categorization (and in some cases production) of news stories. What is particularly tantalizing though is that machine learning is, at its heart, an empirical tool. Given the similarity to tools we know, it is tempting to ask whether it is merely old (econometric) wine in a new (machine learning) bottle.In the courses, we will argue that it is not. Far from it, we will discuss how these tools can powerfully improve and expand on the kind of empirical work we tend to do. At the same time, we will discuss their limitations and how they fit into the “econometric toolbox”. At a high level, this class will address these three questions:
How does machine learning work? What can machine learning tools do that our current toolbox cannot? Where can machine learning be used to generate new research output?
The syllabus for the course:
The first skills camp was a Hands-On Introduction to Data Scraping offered by Carsten Schwemmer and was organized in cooperation with the Berlin Doctoral Program in Economics and Management Science (BDPEMS). The course took place from September 27th to October 2nd at HU Berlin and was organized as a block event consisting of 4 all-day sessions. 20 young researchers practised various different methods for extracting data from web sites. Further details about the content of the course can be found here.