Courses

Currently, all of our training courses are being held online.

All of our courses are hosted by expert certified trainers and research professionals who teach through a mix of demonstrative and practical sessions to provide high-class, practical training.

You can register for our courses online. To discuss any of our courses or specific training requirements, please call +44 (0) 20 8697 3377 .

Instrumental Variables and Structural Equation Modelling using Stata - Online

1st - 2nd March 2021 Online 2 days (1st March 2021 - 2nd March 2021) Stata

Course Overview

Presented by Dr. Giovanni Cerulli

Course Timetable: 10am - 12pm & 2pm - 4pm(London time)

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.

Econometrics of Program Evaluation Using Stata

8th & 9th March, 2021 Online 2 days (8th March 2021 - 9th March 2021) Stata

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.

Data Science for Financial Markets - Co-Developed with Lancaster University

12th - 13th March 2021 Online 2 days (12th March 2021 - 13th March 2021) EViews

Presented By: Dr. Malvina Marchese (Cass Business School, City, University of London)

At times of great uncertainty for financial markets, this course provides participants with an understanding of the time series methods involved in modelling and forecasting financial markets volatilities. Participants will learn how to build, estimate and assess alternative models of volatility of financial time series. Each concepts will be explained from an econometric perspective and by means of many examples and applications in EViews.

Climate Econometrics Online Spring School

15th March - 17th March 2021 Online 3 days (15th March 2021 - 17th March 2021) OxMetrics

Presented by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry.

The course 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.

The framework, its basic concepts and implications will be explained for modelling evolving processes that are also subject intermittently to outliers and structural breaks. Applications to empirical climate time series will demonstrate the approach. The Climate Econometrics international webinar by Zack Miller will be included on Tuesday 16.

Join us for the 23rd Dynamic Econometrics Conference following this Spring School, 18th - 19th March 2021.

Panel Data Models in EViews

19th - 20th March, 2021 Online 2 days (19th March 2021 - 20th March 2021) EViews

Presented by Dr. Malvina Marchese

Panel data econometrics has developed rapidly over the last decades.

Longitudinal data – both with a large number of units tracked for a short period and with a relative small number of units for a long time - 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 econometrics with EViews, covering linear models with exogenous and endogenous variables, static and dynamic linear models. All the traditional static and dynamic econometric techniques are discussed (fixed effect, random effect, GMM, GLS) together with some more advanced topics, such as serial correlation, stationarity and cointegration. The focus of the course is applied and all the topics are demonstrated in EViews using micro and macro panel data sets.

An Introduction to Panel Data Analysis using Stata

26th - 27th March 2021 Online 2 days (26th March 2021 - 27th March 2021) Stata

Presented By: Dr. Malvina Marchese (CASS Business School, London)

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.

Vector Autoregression (VAR) Modelling using EViews (Online)

29th - 30th March 2021 Online 2 days (29th March 2021 - 30th March 2021) EViews

Presented by Prof. Lorenzo Trapani (University of Nottingham)

The course offers an intermediate/advanced level overview of stationary VARs, cointegrated VARs and the VECM, and an introduction to Structural VARs (SVARs). It is a mixture, with equivalent weights, of methodology and practice, and each session is complemented by a data example. The SVAR part is also based on discussing several examples which are commonly encountered in macroeconometrics and monetary economics.

The course is aimed at practitioners and applied researchers in general who wish to either have a comprehensive introduction to the practical use of VARs and their variants, or a more rigorous understanding of these tools.

Risk Modelling with EViews

2nd - 3rd April 2021 Online 2 days (2nd April 2021 - 3rd April 2021) EViews

Overview

Presented by Dr Malvina Marchese (Cass Business School, City, University of London)

Risk modelling is about modelling and quantification of risk. For the financial industry, the cases of credit-risk quantifying potential losses due, e.g., to bankruptcy of debtors, or market-risks quantifying potential losses due to negative fluctuations of a portfolio's market value are of particular relevance.

The aim of this course is to offer a comprehensive introduction to risk modelling and forecasting with EViews 12. The course offers one introductory day on risk modelling and forecasting via parametric methods and then builds on it to discuss the econometrics methods for stress testing, Value at Risk and fundamental measures of risk.

By the end of the two-day online course participants should be able to model and forecast risk with EViews 12 proficiently.

An Introduction to Machine Learning using Stata - Co-Developed with Lancaster University

7th - 8th April 2021 Online 2 days (7th April 2021 - 8th April 2021) Stata

Presented by Dr. Giovanni Cerulli

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 from rows of large, and possibly noisy data. The teaching approach will be 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.

Building Your Own Models In EViews

9th - 10th April 2021 Online 2 days (9th April 2021 - 10th April 2021) EViews

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.

Data Science For Health Researchers: An Introduction to Stata

15th - 16th April 2021 Online 2 days (15th April 2021 - 16th April 2021) Stata

Overview

Presented by: Dr. Vincent O'Sullivan

This course is for professionals and researchers who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software. The participants will be introduced to Stata’s interface. They will be shown how manage and prepare datasets for analysis. The fundamentals of data analysis and visualization will also be taught. Then, the participants will be introduced to two of the main data analysis tools: linear regression and logistic regression. Participants will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from health research.

Machine Learning for Prediction and Causal Inference - Masterclass

20th - 21st April, 2021 Online 2 days (20th April 2021 - 21st April 2021) 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.

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