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This module covers core techniques of econometric theory, including multiple linear regression, maximum likelihood estimation, hypothesis testing, misspecification testing and large sample theory. The module aims to provide a solid foundation in econometric theory and its applications. Students will develop analytical skills and understand the theoretical properties of econometric techniques. The module emphasises the importance of understanding econometric methods for economic analysis. It prepares students to conduct and interpret econometric research.

This module provides hands-on training in the use, presentation, and interpretation of economic data, including time series, cross-section, and panel data. It covers descriptive statistics, hypothesis testing, regression analysis, panel data, dynamic modelling, and time series models. The module aims to provide competence in econometric data analysis. Students will develop skills in using econometric software packages. The module emphasises the importance of understanding economic data analysis techniques. It prepares students to analyse and interpret economic data effectively.

This module introduces artificial intelligence and machine learning for economics, with a focus on using Python and modern AI tools to analyse unstructured datasets. Students learn how economists can use large language models and other recent AI advances to turn text and images into usable economic data to answer real-world research and policy questions. The module examines developments in AI and the changing economics of AI. Through hands-on examples, students develop practical skills and a critical understanding of the opportunities, limitations, and implications of AI for economic research and policy.

The module introduces the core methods of machine learning and their application to economic research. Topics covered include regularisation, decision trees and ensemble methods, support vector machines, interpretable machine learning, dimensionality reduction via principal components and clustering, double-debiased machine learning for causal treatment effect estimation, and neural networks. Computer classes provide hands-on implementation experience using Python.

A period of research and study designed to allow you to demonstrate familiarity with a particular area of economic theory or policy, or of applied economics or econometrics, and the ability to apply a specific analytical and/or empirical technique.

This module provides a rigorous treatment of core topics in modern microeconomic theory. Students will explore market structure, examining how firms behave and interact under different competitive environments, from monopoly and oligopoly through to more complex market configurations. Game theory is studied as a general framework for strategic interaction, covering key solution concepts and their applications to economic settings. The module also addresses information economics, analysing how asymmetric information shapes market outcomes, contract design, and strategic behaviour.

This module covers the theory and empirics of modern macroeconomic models, divided into long-run and short-run macroeconomics. The long-run part covers the neoclassical growth model, aggregate productivity measurement, endogenous growth theory, and structural transformation. The short-run part covers DSGE modelling, the real business cycle model, the New Keynesian framework, and extensions motivated by the financial crisis. Students develop skills in constructing and analysing macroeconomic models and connecting them to empirical evidence, preparing them to engage with the research and policy debate in macroeconomics.

This module develops the quantitative toolkit used in modern applied microeconomics. Students will engage with the theoretical foundations of causal inference and learn to implement core identification strategies, including instrumental variables, difference-in-differences, and regression discontinuity designs, used to evaluate economic behaviour and policy. Emphasis is placed on bridging econometric theory with empirical practice, equipping students to critically assess published research and conduct original analysis using real-world data. Prior exposure to statistics and introductory econometrics is assumed.

This module studies macroeconomic policies primarily through the lens of general equilibrium models. It first covers the classical and New Keynesian models, the interaction between monetary and fiscal policies, and the design of optimal macroeconomic policies. Students develop quantitative skills by solving models numerically and running policy simulations. The module also examines the effects of policy shocks in the open economy, examining issues such as exchange-rate determination and current accounts. An emphasis is placed on developing intuition behind international economic interactions. 

This module examines topics in econometric analysis, including topics in econometric theory and econometric modelling techniques with applications to areas such as finance and macroeconomics. It addresses contemporary issues in time series econometrics and aims to provide a solid foundation in time series analysis. Students will develop analytical skills and understand empirical time series research methods, together with theoretical properties of stationary and non-stationary time series models. The module emphasises the importance of understanding econometric techniques for economic analysis. It prepares students to conduct and interpret econometric research in areas such as finance and macroeconomics.

This module introduces research methods of experimental economics and their applications.  Students will learn experimental design principles and various core tools of experimentation. The module will illustrate different uses of experiments, such as testing theory or informing policy. The module explores the scope and limits of experiments and aims to develop skills for critically assessing experimental designs and results. In a practical part, students will design, implement, present and report a small-scale experiment. The module aims to provide a nuanced understanding of experimental methods and to provide students with hands-on experience in the conduct and interpretation of experimental research. 

This module introduces empirical methods for evaluating economic development policy interventions, organised into three blocks. The first block presents the design, implementation and analysis of Randomised Controlled Trials with applications to issues such as taxation, microcredit, labour and education. The second block addresses trade policy instruments, trade and non-trade barriers, trade costs and regional trade agreements, with methods including partial equilibrium and gravity models. The final block covers public spending, aid, FDI and multinationals. The module equips students to assess policy reforms, understand econometric and other empirical approaches for policy evaluation, and interpret the impacts of development policies in practice. 

This course studies global economic interactions through the lenses of international trade and finance. It begins with the foundations of trade theory, including comparative advantage and models emphasizing firm behaviour. It will also analyse trade policy, and the political economy of policy interventions. The course then examines macroeconomic linkages across countries, covering exchange rates, balance of payments adjustment, capital mobility, and open-economy monetary policy. Students engage with empirical evidence and policy debates to understand globalization, interdependence, and international adjustment mechanisms.