Courses

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, as well as a recording of the training session, that will be live for 30 days.

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.

• How to structure your dissertation
• Abstract and introduction in depth discussion and examples
• How to get a smart literature review –discussion of successful examples
• Build, estimate and forecast from linear regression, time series and panel models using STATA
• Understand and critically present and discuss your results

Do you have course specific questions? Email our team [email protected]. If you have course content specific questions, you are welcome to reach out to the course tutor here: [email protected].

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.

The aim of this course is to provide participants with an in-depth understanding of the fundamental concepts of time series modelling and forecasting and with the practical skills to use Stata to model and forecast 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.

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.

Presented by: Lecturer/s Malvina Marchese , Cass Business School , London

Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. Its primary objective is that of turning information into knowledge and value by “letting the data speak”. To this purpose, machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures and analytical solutions. Computationally, machine learning was unfeasible a few years ago, it is a product of the computerised era, of today's machines' computing power and ability to learn. It is also a product of hardware development and continuous software upgrading. This course is a primer to machine learning techniques in Eviews.

The latest edition of Eviews 11 offers various packages to perform machine learning. After the course, participants are expected to have an improved understanding of EViews' potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks and specifically to master model selection techniques.

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Machine Learning, Regression analysis