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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.

Presented by James Gallagher and Sandro Leidi

Choosing an appropriate sample size is a common problem and should be given due consideration in any research proposal, as an inadequate sample size invariably leads to wasted resources.

This course gives a practical introduction to sample size determination in the context of some commonly used significance tests.

Examples from a scientific background are used to highlight the problems associated with sample size determination and suggest potential solutions.

Formulae and algebraic notation are kept to a minimum.

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 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: 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: Dr. 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.

Presented by Prof. Christopher F. Baum, Boston College & Dr Vincent O'Sullivan, Lancaster University

This course will be delivered as an online webinar, via Zoom.

This course is taught in two sections. The first, is for Stata users–professionals and researchers from all academic disciplines–who would like to use Stata programming techniques to enhance the efficiency and reliability of their research. The course assumes familiarity with Stata’s command-line interface and the use of do-files and log files to produce reproducible results. The participants will learn how to use do-file programming techniques effectively, including topics such as local and global macros, r-returns and e-returns, implicit and explicit loops and debugging techniques.

The second, is for users who have completed the companion course Introduction to Stata Programming and would like to use more advanced features of the Stata and Mata programming languages. The course assumes familiarity with Stata’s command-line interface and the use of do-files and log files to produce reproducible results. Mata programming techniques will illustrate how this language can be used to simplify and accelerate computations.

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