Research Summer School

About

The Core Research Methods for Business and Economics offers three intense courses, where researchers and practitioners from around the world can come together to learn and build relationships.

Data Analysis using STATA - course date 24-29 June 2019 (6 days)
Machine Learning and Data Mining with Python - course date 1-6 July 2019 (6 days)
Structural Equation Modelling (SEM) - course date 8-13 July 2019 (6 days)

Target group

The program targets
Academics
Professionals
PhD students/DBA students
Post-Doctoral students
Post-graduates students

Course aim

The Data Analysis using STATA course (24-29 June) will improve your data handling, analysis and interpretation skills needed to carry-out your own empirical project. You will also gain familiarity with gathering and using large secondary data sets and learn how big data can be utilized.

During the course the fundamentals of regression analysis as well as applied topics related to estimation and inference for probability models, panel data, difference-in-differences and instrumental variables will be covered. Throughout the course, each topic is explained using examples based on real data. The course is hands-on, all computer sessions will be used to replicate results from research articles in finance and economics.

The course will rigorously introduce you to the methods but without the mathematical details behind the models. You will gain a clear understanding of when and how to apply the different models and of their limitations. The course puts an extra emphasis on visualizing regression models and will use graphical intuition to improve the understanding of more advanced topics.

The 6-day Machine Learning and Data Mining with Python (1-6 July) course will familiarize participants with algorithms using the Python programming language. You will learn to recognize the correct machine learning approach for a given problem; furthermore, participants will learn how to implement several different algorithms. The lab classes will use R/Python/Octave and public data sets.

The 6-day course in Structural Equation Modelling (SEM) (8-13 July) will familiarize participants with Structural Equation Modelling (SEM) and how to use SEM in testing theories that contain multiple equations involving dependence relationships. Structural equation modelling (SEM) is a statistical methodology that takes a hypothesis-testing (i.e., confirmatory) approach to the multivariate analysis of a structural theory bearing on some phenomenon. Structural equation models are used to assess unobservable “latent” constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables. SEM can examine a series of dependence relationships simultaneously. SEM is widely used in the social sciences.

Fee info