Dr Melvyn Weeks (University of Cambridge)

Associate Professor of Economics University of Cambridge

Dr. Melvyn Weeks is an Associate Professor at the Faculty of Economics, University of Cambridge and a Fellow of Clare College. Dr Melvyn Weeks' research interests include microeconometrics, Discrete choice models, Mixture models, Differentiated demand systems in economics and marketing and Bayesian model averaging, models and tests for convergence.

Melvyn is also a developer of the forthcoming Masters course Economics and Data Science starting at the University of Cambridge in 2024.


Training & Consultancy

Melvyn Weeks is a key training associate for Timberlake Consultants, leading both public attendance and tailored onsite courses. His recent involvement includes co-leading our annual Econometrics Summer School, Cambridge, as well as delivering a wide range of public attendance training courses, including Machine Learning & Causal Inference - A Masterclass, Microeconometrics using Stata: Linear Models and Microeconometrics using Stata: Nonlinear Models.


Feedback and Testimonials

Participant feedback from past Econometrics Summer School, Cambridge

If I was ever wondering what Einsteins paraphrased quote "to make things as simple as possible, but not simpler" meant, you certainly made that clear to me these past days. What you do is not merely lecturing / you put knowledge in the heads of the audience. Thank you for the extremely educational lectures and the numerous A-HA moments.

I hope I will be able to attend the continuation of your course [Microeconometrics using Stata: Nonlinear Models] - It was a great learning experience; thank you for delivering the course so well.

It is genuinely an honour to be attending your lecture.

It was definitely the best of the three courses for me and if my employer allows I will be happy to attend more courses taught by Dr. Weeks.

Melvyn Weeks was just amazing. He really knows how to teach econometrics.

Prof. Weeks is a very good teacher who makes the lectures very interesting (not boring) and he tries to repeat, summarise, show examples, link topics together and build on the basics and involve the students. These features bring the highest value added in terms of course efficiency.

Very good course material; Lecturer is very knowledgeable; he also made a lot of effort to engage the class, spoke clearly, and emphasised and repeated points before proceeding to the next topic. This approach helped me to understand quite complex material early on.

The professor provided a very nice presentation of different models. The professor has a complete knowledge of all these models and therefore was able to explain how they are related which made it easier to understand these models. In addition, the professor explained very well the estimation and testing of these models. Within the lectures the professor explained very well the intuition and the procedures of the statistical tests of the models covered.

The practical part that was in perfect balance with the theoretical part, making the course material much easier to absorb and the overall optimal organisation of the lectures (length of both classes and breaks).


Participant feedback from past Microeconometrics using Stata: Linear Models courses

The course material provided i.e. handouts were such that you could follow clearly, plus the one day of bringing everybody up to speed with Stata [referring to pre-course Stata Fundamentals session].

Lots of material provided, so that I can go back over it later to consolidate the learning/refresh my memory.

Melvyn gave an unparalleled course in econometrics. He conveyed key ideas/fundamentals with clarity, rigour, and in an intuitively accessible manner.


Participant feedback from past Microeconometrics using Stata: Nonlinear Models courses

The course was great! From my perspective Melvyn has designed the course to what I eed as opposed to what I want. I came into the course to pick up some of the practical methods to apply non-linear models but in the three days (which was tough) I got a birds eye-view of how to understand non-linear models and then apply them. As a young researcher this was my greatest take-away from the course.

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