Courses

Presented By: Dr. Melvyn Weeks (University of Cambridge)

This course will review the application of machine learning techniques to both prediction problems and so-called causal problems where a firm or policy maker needs to understand the impact of some form of intervention on a heterogeneous population. We contrast a modelling approach where the analyst makes certain assumption on model specification, including functional form, with an approach where the data mechanism is presumed unknown. In this context we consider the econometrician’s concern for internal validity, alongside the focus within machine learning of ensuring that a model is robust in the sense of generalising to unseen data (external validity).

The course will focus upon topics at the intersection of machine learning and econometrics, covering a mix of theory and applications. In making the distinction between models which are used to solve a prediction problem and models which are used to estimate some form of causal effect, we introduce participants to identification strategies in econometrics. In covering two broad areas where machine learning is used, namely prediction, classification and causal effects, for each case we link the exposition to parametric bench- marks. For Machine Learning models in prediction, classification and causal effects we provide examples using Stata, R and Python.

Overview

Presented by: Dr. Vincent O'Sullivan

This course is for professionals and researchers who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software. The participants will be introduced to Stata’s interface. They will be shown how manage and prepare datasets for analysis. The fundamentals of data analysis and visualization will also be taught. Then, the participants will be introduced to two of the main data analysis tools: linear regression and logistic regression. Participants will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from health research.

Presented by Dr. Malvina Marchese (Cass Business School, City, University of London)

The course follows the Introduction to Panel data Analysis with Stata and aims to provide participants with a theoretical and practical understanding of advanced panel methods, i.e. non-linear panel models.

Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results. Hands-on sessions with many practical examples and exercises to discuss the different methodologies on panel data analysis.

Presented by Dr. Anis Samet

This workshop is an introduction to regression analysis with categorical dependent variables using the Stata software. It will cover the most commonly used regression models for categorical outcomes: binary logit and probit, ordinal logit, and multinomial logit.

The course assumes that attendees have prior knowledge of common commands in Stata to organize and handle data and undertake standard regression techniques. Nevertheless, this is an introductory course.

The aim of this course is to provide participants with an in-depth understanding of how a good MSc dissertation should look, and how to easily use Stata to obtain any required econometrics.

Participants will receive a free temporary Stata license for the duration of the course

The course is meant for any MSc student writing their MSc dissertation, who needs guidance on the best structure, and most suitable econometric methods to apply. No previous knowledge of Stata is required.

Presented by Robert Grant

In healthcare, economics or commercial data science, analysts of all backgrounds have to build models for their data. Models help us to understand causal relationships and forecast the future, but as statistician George Box put it, "all models are wrong, but some are useful". How do you know if your models are useful? Or maybe even wrong? This one-day course is an introduction to the techniques used in model building.

Presented by: Dr. Giovanni Cerulli

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.

It will provide participants with the essential tools, both theoretical and applied, for a proper use of recent micro-econometric methods for policy evaluation and causal modelling in situations where the standard treatment setting poses limitations.

More specifically, the course will focus on these approaches: (i) Difference-in-differences (DID) with time-varying and time-fixed binary treatment; (ii) the Synthetic Control Method (SCM) for program evaluation, suitable when datasets on many times and locations are available; (iii) models for multivalued and quantile treatment effect estimation.

After attending the course, the participant will be able to setting up and managing a correct evaluation design using Stata, by identifying the policy framework, the appropriate econometric method to use interpreting correctly the results. The course will provide various instructional examples on real datasets.

Presented by Dr. Giovanni Cerulli

This course will focus on three specific techniques: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).The teaching approach will be mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.

Course Overview

Presented by Dr. Giovanni Cerulli

This course provides participants with the essential tools, both theoretical and applied, for a proper use of instrumental variables (IV) and structural equation models (SEM) for statistical causal modelling using Stata.

Our Stata Summer school provides a very popular and flexible course framework allowing cost-effective attendance at any course separately, or the entire school. This is a great opportunity for students, academics and professionals to expand their econometrics skills and learn how they can apply econometrics and statistics from professionals pioneering research at the forefront of their specialist fields.

For the first time, we will be running our Summer school entirely online, so you can join from the comfort of your home, anywhere in the world.

Live sessions 10.00-12.00; 13.00-15.00; 15.30-17.00 including Q&A

Presented By: Dr. Austin Nichols

This course is for professionals and researchers from all academic disciplines who wish to improve their use of Stata.

Participants will be introduced to Stata and causal inference using practical examples. The fundamentals of data analysis and visualization will also be taught, using appropriate Stata commands and programming techniques

The aim of this course is to provide participants with an in-depth understanding of the fundamental concepts behind time series modelling and forecasting with Stata. The course will provide the practical skills necessary to use Stata for modeling and forecasting of economic time series.

This comprehensive webinar is hosted through Zoom and runs over a total of 9 hours, with 4 hours each day (2 in the morning and 2 in the afternoon) with an extra Q&A session on the second day.