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 .

How to Write Your Dissertation with Stata

17 - 18 August 2020 Online 2 days (17th August 2020 - 18th August 2020) Stata

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 info@timberlake.co.uk. If you have course content specific questions, you are welcome to reach out to the course tutor here: malvinamarchese@timberlake.co.uk.

Stata Programming Workshop: Introduction and Advanced

3 & 4, 8 & 9 September 2020 Online 4 days (3rd September 2020 - 9th September 2020) Stata

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.

Time series Analysis with Stata

14 - 15 September 2020 Online 2 days (14th September 2020 - 15th September 2020) Stata

The aim of this course is to provide participants with an in-depth understanding of the fundamental concepts of time series modelling and forecasting and with the practical skills to use Stata to model and forecast economic time series.

This comprehensive webinar is hosted through Zoom and runs over a total of 9 hours, with 4 hours each day (2 in the morning and 2 in the afternoon) with an extra Q&A session on the second day.

Introduction to Panel Data Analysis with Stata (Online)

18 September (8 hours, 2020) Online 1 day (18th September 2020 - 18th September 2020) Stata

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.

Machine Learning with Eviews

25-26 September 2020 Online 2 days (25th September 2020 - 26th September 2020) EViews

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.

Introduction to Linear Models using Stata (online)

30 Sept - 1 Oct 2020 Online 2 days (30th September 2020 - 1st October 2020) Stata

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.

Advances in causal inference using Stata (ONLINE)

5 - 6 October 2020 Online 2 days (5th October 2020 - 6th October 2020) Stata

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.

What Sample Size do I need? With Stata (online)

8 October 2020 Online 1 day (8th October 2020 - 8th October 2020) Stata

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.

Time Series Econometrics (online)

23 - 24 Oct 2020 Online 2 days (23rd October 2020 - 24th October 2020) EViews

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

Machine Learning using Stata: Introduction & Advanced, Co-Developed with Lancaster University

26 - 27 October & 9 - 10 November 2020 Online 4 days (26th October 2020 - 10th November 2020) Stata

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.

Regression Modelling using Stata

29 - 30 October 2020 Online 2 days (29th October 2020 - 30th October 2020) Stata

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.

Macroeconomic Density Forecasting & Nowcasting

2 - 3 November 2020 (10-12pm & 2-4pm London time) Online 2 days (2nd November 2020 - 3rd November 2020) EViews

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.

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