Courses

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 .

2020 Stata Econometrics Summer School

29 June - 1 July 2020 Lisbon School of Economics & Management 3 days (29th June 2020 - 1st July 2020) Stata

Presented By: Pierre Hoonhout, School of Economics and Management, University of Lisbon

The Summer School aims to provide the full set of tools and techniques that any modern applied economist needs to know, and how to apply them using Stata. The Summer School is suitable for Academic Staff, Masters/PhD students and professionals that need to analyse data. The course will be taught in English.

Macroeconomic Density Forecasting & Nowcasting

July, 1 & 2 2020 (New York, USA) New Horizons, Computer Learning Centre, New York City, USA 3 days (1st July 2020 - 2nd July 2020) EViews

Presented By: Professor Andrea Carriero (Queen Mary, University of London)

Whether you deal with forecasting at a Central Bank, public institution, bank or consultancy firm; or you use forecasting techniques in your research, this is the perfect course to bring you up to date with the latest methods in the forecasting profession.

Regime switching and mixed frequency models in EViews

5 July 2020 1 day (5th July 2020 - 5th July 2020) EViews

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

This workshop provides a comprehensive introduction to two advanced econometric methodologies that in recent years have gained wide popularity in modelling and forecasting time series data: switching regression models and mixed data sampling models.

Score Driven Time Series Models

9 July 2020 1 day (9th July 2020 - 9th July 2020) OxMetrics

Presented By: Andrew C. Harvey (University of Cambridge)

Dynamic conditional score (DCS) - or Generalized Autoregressive Score (GAS) - models have developed rapidly over the last ten years and continue to be a fruitful area for research. They offer a united and comprehensive theory for a class of nonlinear time series models in which the dynamics of a changing parameter, such as location or scale, are driven by the score of the conditional distribution.

Stata Programming Workshop

7 - 9 September 2020 London, Work Foundation 3 days (7th September 2020 - 9th September 2020) Stata

Presented By: Professor Christopher F. Baum

Stata Programming Workshop is an opportunity for graduate students, academics, researchers and professionals to expand their knowledge of Stata. This 3-day course introduces programming skills to those who have never written a program in Stata.

Advances in Causal Inference using Stata

14 & 15 September 2020 Online 2 days (14th September 2020 - 15th September 2020) Stata

Presented By: Dr. Giovanni Cerulli, IRCrES-CNR

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.

An Introduction to Machine Learning using Stata

26-28 October 2020 Online 3 days (26th October 2020 - 28th October 2020) Stata

Presented By: Dr. Giovanni Cerulli, IRCrES-CNR

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.

Causal Inference using Stata

3-4 December 2020 2 days (3rd December 2020 - 4th December 2020) Stata

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

Time Series Analysis & Modelling using Stata

10 - 11 December 2020 TBC 2 days (10th December 2020 - 11th December 2020) Stata

Presented By: Dr. George Naufal (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 15. The course is based on the textbook by S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata, Stata Press Publication.

Climate Econometrics Spring School - 2021

Spring 2021 George Washington University, Washington, D.C. 2.5 days (7th March 2021 - 9th March 2021) OxMetrics

Presented By: Prof. Sir David F. Hendry, Dr. Jennifer Castle & Dr. Jurgen Doornik

Castle, Doornik and Hendry deliver this new course to our schedule which provides an introduction to the theory and practice of econometric modelling of climate variables in a non-stationary world. It covers the modelling methodology, implementation, practice and evaluation of climate-economic models.

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