Training Courses

Mixed modelling is a modern and powerful data analysis tool for modelling clustered data, typically used for modelling data collected in studies where the levels of a factor are considered to be a random selection from a wider pool, or in the presence of a multi-level structure with different levels of variability.

The course will focus on medical and health related applications of mixed modelling. Specific applications include multi-centre trials and cross-over trials in addition to the analysis of repeated measurements.

The objective of this course is to provide participants with a gentle introduction to the essential toolset, mainly applied, for the implementation of simple structural equation models (SEM) for causal modelling in Stata, using the Stata package sem.

This two-day course introduces participants to Panel Data analysis and will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from economic, social, and medical research.

This course is a primer to machine learning techniques using Stata. Stata owns today various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with (and knowledgeable of) Stata potential to draw knowledge and value from rows of large, and possibly noisy data. The teaching approach will be based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.

This course will cover topics at the intersection of machine learning and econometrics.

Our web-based Introduction to Panel Data Analysis with Stata course provides an overview of the most used panel data techniques. The course is ideal for beginner/intermediate level user who wants to learn how implement panel data estimation with Stata commands.

Our web based Introduction to Time Series Analysis with Stata course provides an introduction to most important Stata’s Time Series Commands.

The course is ideal for beginner/intermediate level user who wants to learn how to analyse time-series data and estimate univariate time-series models. The course is based on the textbook Financial Econometrics Using Stata by S.Boffelli and G.Urga (2016), Stata Press

The course focuses on practical programming needs arising when dealing with large datasets, multiple data sources and the programming tools which may help in routinising complex tasks and automating pieces of your work.

This course is a primer to machine learning techniques using Stata. Today, various machine learning packages are available within Stata, but some of these are not known to all Stata users. This course fills this gap by making participants familiar with Stata's potential to draw knowledge and value from rows of large, and possibly noisy data. The teaching approach will be based on the graphical language and intuition more than on algebra. The sessions will make use of instructional as well as real-world examples, and will balance theory and practical sessions evenly.

The aim of this course is to provide students with the theoretical knowledge of regression analysis and with the practical skills to implement regression analysis with econometric software.

Econometric modelling for causal inference and program evaluation have witnessed a tremendous development in the last decade, with new approaches and methods addressing an expanding set of challenging problems, both in medical and the social sciences. This course covers some recent developments in causal inference and program evaluation using Stata.