Courses

Presented by Sandro Leidi & James Gallagher

This course is running online, via Zoom.

Mixed models are a modern powerful data analysis tool to analyse clustered data, typically arising in studies where the levels of a factor are a random selection from a wider pool, or in the presence of a multi-level nested structure with different levels of variability.

Potential benefits of mixed models are greater generalisability of results and accommodation of missing values. In particular, mixed models have been used in clinical trials to analyse repeated measures, where measurements taken over time naturally cluster according to patient.

The course will illustrate medical and health related applications of mixed modelling, such as multi-centre trials, cross-over trials, and the analysis of repeated measures. The course focuses on the linear mixed model, assuming normally distributed data, and on how to fit it and interpret its results.

Only essential theoretical aspects of mixed models will be summarised.

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 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.

Delivered by Dr. 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”. Machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures and analytical solutions. This course is a primer to our "Machine Learning Techniques in EViews short-course.

The latest edition of EViews 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, becoming able to master research tasks, specifically model selection techniques.

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

Course Overview: Part one

Recent years have witnessed an unprecedented availability of information on social, economic, and health-related phenomena. Researchers, practitioners, and policymakers have nowadays access to huge datasets (the so-called “Big Data”) on people, companies and institutions, web and mobile devices, satellites, etc., at increasing speed and detail.

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 graphical inspection more than tight assumptions, algebraic development, and analytical solutions. Computationally unfeasible few years ago, machine learning is a product of the computer’s era, of today machines’ computing power and ability to learn, of hardware development, and continuous software upgrading.

This course is a primer to machine learning techniques using Stata. Stata owns today various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with (and knowledgeable of) Stata potential to draw knowledge and value form row, large, and possibly noisy data. The teaching approach will be mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.

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

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

Course Overview: part 2

This course will focus on three specific techniques not covered in the first-part 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 than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.

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

• (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.

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. 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.

Course Overview

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.

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.

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.

This course will be running online, via Zoom webinar.

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.