MSc — 2027 entry Economics (Econometrics and Big Data)

This module provides an in-depth introduction to the building blocks of graduate-level microeconomics. It introduces students to the modelling of economic behavior under certainty and uncertainty and the interplay among economic agents in markets.

This module provides an in-depth review of the building blocks of macroeconomic theories and introduces macroeconomic datasets at the graduate level. 

This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting. The techniques are applied to real data making use of the econometric packages.

The first part of the module is designed to provide the necessary foundation in mathematical and statistical techniques for the study of economics at graduate level. The second part provides an introduction to programming using specialist programming software. Students will learn how to use numerical methods in the context of mathematic optimisation and data analysis.

The module equips students with the recent machine and statistical learning toolkit for modern empirical economics and quantitative finance analysis. It is practical in orientation and rigorous in execution, developing the professional competence to implement state-of-the-art methods, interpret their output critically, and communicate findings to both technical and non-technical audiences - skills of direct relevance to careers in asset management, central banking, economic consultancy, and policy institutions.The core techniques are introduced in sequence, each grounded in statistical theory and connected to professional practice.  For instance, students will study dimension reduction, encompassing regularisation methods such as ridge regression, the LASSO, the elastic net, and factor models. They will learn how these tools are used in asset management to select pricing factors and in central banks and forecasting institutions to handle large macroeconomic datasets. Students will study tree-based ensemble methods, including random forests and gradient boosting, and learn how they are applied in credit risk departments,  quantitative hedge funds, and policy research to capture non-linear relationships that standard econometric models miss. Students will also study kernel methods, including support vector machines and generalised additive models, and learn how these are used wherever an economic relationship requires a flexible, interpretable specification that does not impose an arbitrary functional form. Finally, students will study rigorous model evaluation - cross-validation, multiple-testing corrections, and model confidence sets - and understand why these procedures are a professional requirement in any setting where analytical findings inform decisions.Throughout, statistical foundations are developed carefully alongside applications, so that students understand not only how to use each method but when it will fail and at what cost.

This module introduces students to microeconometric models for limited dependent variables, panel data methods, and causal inference techniques. A strong emphasis is placed on the empirical applications of these techniques and their intuition.

This module provides an overview of research methods employed in economics, laying the foundations for the dissertation, and guides students on how to carry out independent research.