Currently, all of our training courses are being held online.

All of our courses are hosted by expert certified trainers and research professionals who teach through a mix of demonstrative and practical sessions to provide high-class, practical training.

You can register for our courses online. To discuss any of our courses or specific training requirements, please call

+44 (0) 20 8697 3377 .

Machine Learning for Prediction and Causal Inference - Masterclass

7th - 9th December, 2020 Online 3 days (7th December 2020 - 9th December 2020) Stata

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.

Econometrics of Program Evaluation Using Stata

30th November - 1th December, 2020 (10am - 12pm & 2pm - 4pm, London time) Online 2 days (30th November 2020 - 1st December 2020) Stata

Presented By: Dr. Giovanni Cerulli

This course will provide participants with the essential tools, both theoretical and applied, for a proper use of modern micro-econometric methods for policy evaluation and causal counterfactual modelling under both assumptions of “selection on observables” and “selection on unobservables”. The course will cover these approaches: Regression adjustment (parametric and nonparametric), Matching (on covariates and on propensity score), Reweighting and Double-robust methods, and Difference-in-differences methods.

Instrumental Variables and Structural Equation Modelling using Stata - Online

3rd - 4th December, 2020 (10am - 12pm & 2pm - 4pm) Online 2 days (3rd December 2020 - 4th December 2020) Stata

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.

Time Series Analysis & Modelling Using Stata

10th - 11th December, 2020 (London time 3pm - 11pm) Online 2 days (10th December 2020 - 11th December 2020) Stata

Presented By: Dr George Naufal (Public Policy Research Institute (PPRI), Texas A&M University)

Time series data are nowadays collected for several phenomena in social and empirical sciences. Initially collected at year or quarter level, time series data are now used by marketing analytics, financial technology, and other fields in which data are collected at much smaller intervals (daily, hourly and even by the minute). This course focuses on the fundamental concepts required for the analysis, modelling and forecasting of time series data and provides an introduction to the theoretical foundation of time series models alongside a practical guide to the use of time series analysis techniques implemented in Stata 16. The course is based on the textbook by S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata, Stata Press Publication.

Stata Winter School Online

14th - 19th December, 2020 Online 5 days (14th December 2020 - 19th December 2020) Stata

Course Overview

The Stata Winter School consists of a series of one and two-day courses which can be taken individually or as a whole as required. The School is aimed at students, academics and professionals who want to develop and strengthen their data processing, programming, graphics and statistical skills using Stata. All of the courses are taught interactively using a blend of theory, follow-along demonstrations and exercises.

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

The course timetable: 5.5 hours of live teaching over 3 sessions: 10.00-12.00; 13.00-15.00; 15.30-17.00 (GMT). Each session will include time for Q&A.

Panel Data Econometrics - in collaboration with Lancaster University (online)

Date TBC Online 2 days (28th January 2021 - 29th January 2021) Stata

Prof. Sébastien Laurent, Aix-Marseille University

Panel data econometrics has developed rapidly over the last decades.

Longitudinal data are more and more available to researchers and methods to analyse these data are in high demand from scholars from different fields.

The course offers a comprehensive overview on panel data methods with Stata, covering static and dynamic linear models.

Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results.

By the end of the two-day on-line course, participants should be able to prepare panel data for the analysis with Stata, choose the relevant model, get the parameter estimates and interpret the results.

Machine Learning using Stata: Introduction & Advanced - in collaboration with Lancaster University (online)

9th - 10th November 2020 Online 4 days (26th October 2020 - 10th November 2020) Stata

Course Overview: Advanced

This course will focus on three specific techniques not covered in the first-part of the course, that is: 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.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including:

  • (i) factor-importance detection,
  • (ii) signal-from-noise extraction,
  • (iii) model-free regression and classification, both from a data-mining and a causal perspective.

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.

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