Courses

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.

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.

This course provides participants with the essential tools, both theoretical and applied, for a proper use of instrumental variables (IV) and structural equation models (SEM) for statistical causal modelling using Stata.

Presented By: Dr. Melvyn Weeks (University of Cambridge)

This course will review the application of machine learning techniques to both prediction problems and so-called causal problems where a firm or policy maker needs to understand the impact of some form of intervention on a heterogeneous population. We contrast a modelling approach where the analyst makes certain assumption on model specification, including functional form, with an approach where the data mechanism is presumed unknown. In this context we consider the econometrician’s concern for internal validity, alongside the focus within machine learning of ensuring that a model is robust in the sense of generalising to unseen data (external validity).

The course will focus upon topics at the intersection of machine learning and econometrics, covering a mix of theory and applications. In making the distinction between models which are used to solve a prediction problem and models which are used to estimate some form of causal effect, we introduce participants to identification strategies in econometrics. In covering two broad areas where machine learning is used, namely prediction, classification and causal effects, for each case we link the exposition to parametric bench- marks. For Machine Learning models in prediction, classification and causal effects we provide examples using Stata, R and Python.

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 16. The course is based on the textbook by S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata, Stata Press Publication.

Course Overview

The Stata Winter School consists of a series of one and two-day courses which can be taken individually or as a whole as required. The School is aimed at students, academics and professionals who want to develop and strengthen their data processing, programming, graphics and statistical skills using Stata. All of the courses are taught interactively using a blend of theory, follow-along demonstrations and exercises.

For the first time, we will be running our winter school entirely online, so you can join from the comfort of your home, anywhere in the world.

The course timetable: 5.5 hours of live teaching over 3 sessions: 10.00-12.00; 13.00-15.00; 15.30-17.00 (GMT). Each session will include time for Q&A.

Presented by Dr. Malvina Marchese

This course is for economists, econometricians and applied researchers who want to go even further in Eviews by coding something that is not implemented in the existing routines such as long memory GARCH models or Value at Risk. In addition, one may wish to make life easier and to ask EViews to perform some repetitive tasks. This is where EViews Programming starts. The goal of this training course is to make life easier, namely to do things with only a small investment instead of learning a completely new language.

This course will be running online, as a Zoom webinar.

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 Malvina Marchese, Cass Business School, City, University of London

In the last decade, Machine Learning models have established themselves as serious contenders in the area of forecasting to classical econometric models. The aim of this advanced machine learning course with EViews is to offer an overview of the most popular Machine lLearning methods for prediction including neural network, decision trees, K nearest neighbourhood, kernel regressions.

The course offers plenty of practical examples that apply to financial Big Data and discusses comparison with traditional econometric forecasting methods such as ARIMA, Markov switching, smooth transition autoregressive models.

The course is EViews based and makes large use of the EViews-Python integration. Participants are not required to have any previous knowledge of python, but should have installed it before joining. The course is intended as an advanced course and attendance to the introductory course, machine learning in EViews, is highly recommended.

Participants will leave with an in-depth understanding of forecasting with machine learning , with the ability to run any python code via the user friendly and graphically advanced EViews interface , with basic python coding skills and plenty of codes examples.

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.

This course will focus on three specific techniques: 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 so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

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.