Courses

Presented By: Dr. George Naufal (Texas A&M University)

Our web-based 'Introduction to Panel Data Analysis with Stata' course provides an overview of the most-used panel data techniques and is ideal for the beginner/intermediate-level user who wants to learn how to implement panel data estimation with Stata commands.

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: Dr. Malvina Marchese

This course is for researchers from all academic disciplines who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software. Participants will be introduced to Stata and will be taught the statistical theory behind linear and non-linear regression methods . Practical sessions will use macro economic and finance datasets.

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: 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. 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.

Delivered by Dr. Malvina Marchese

This online intensive course provides a comprehensive introduction to time series analysis and forecasting with EViews. The course offers a full overview on time series models and forecasting methods, covering a variety of different models including ARMA, ARDL, Regime Switching models, GARCH models and Midas time series regressions. Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results.

Taking a “learning-by-doing” approach, we present the most relevant time series models employing plenty of financial and macroeconomic data examples. The course specifically focuses on forecasting methodologies in macro econometrics and financial econometrics. Participants leave with the know-how on a wide range of time series models and the ability to identify which one to use for a specific modelling and forecasting purpose.

The course is intentionally flexible. The agenda emerges dynamically and depends on the group’s prior background and knowledge of EViews. By the end of the two-day on line course participants should be able to:

• Model and forecast from a univariate AR(FI)MA model
• Model and forecast from a univariate GARCH (including EGARCH, TARCH, APARCH and GJR models)
• Distinguish between stationary and nonstationary series and understand the implications of using nonstationary series;
• Build, estimate and forecast from univariate time series models using Eviews an compare the forecasting performances of the models
• Understand and critically evaluate recent research in time series