What are the distinctive features of less developed economies? How do theories around child labour or inequality explain poverty? What economic policies could alleviate such problems? Understand the issues facing developing countries, examining policies theoretically and empirically. Act as a policy advisor, undertaking research on issues of development economics.

What are the concepts and methods of modern microeconomics? And how can you apply economic reasoning to this? Understand the main principles and theories of modern microeconomics, looking at topics like contract theory, equilibrium concepts in game theory, and market signalling. Learn to apply economic reasoning to these arguments.

What are the main issues facing the modern macroeconomist? How do you critically assess macroeconomic policies? Acquire the necessary tools for macroeconomic analysis, focusing on the important questions faced by macroeconomists today.

Equip yourself with principles of allocation and mechanism design from an operational perspective. Auction design and market microstructure of the stock market, liquidity provision in electronic financial markets such as dark pools, and capital adequacy of centralized clearing platforms are some of the specific applications that will be studied in the first part of this module. During the second part, you will be introduced to complexity economics of self-organisation, network modules, and strategic proteanism. Finally, you'll use network models to study economic interactions.

This postgraduate module equips you with the key tools in modern data science, with a focus on machine learning (ML) and its application to Economics and Finance. The main goal of this module is to enable you to understand how machine learning tools can complement the tools of traditional econometrics and how to apply these techniques to real-world economics and finance problems. By the end of this module you will have: 1. Developed a comprehensive understanding of key concepts in modern machine learning (ML): classification, prediction, supervised and unsupervised learning 2. Demonstrated a critical understanding of the advantages and disadvantages of ML as compared to traditional econometric approaches 3. Applied ML to real-world economics and finance problems, with examples based on: i. Prediction ii. Causal Inference and Policy Evaluation

How do firms interact? What impact does this have on products that are available to you? Or the price that you pay? Understand current thinking on industrial organisation, with a focus on competition policy, regulation and business strategy. Apply analytical models of firm behaviour and strategic interaction to real-life situations.

Despite all the talk about the “death of distance”, geography matters more than ever. This course is a journey through the current economic landscape. We will try to understand the economic forces driving trends in wages, productivity and innovation across cities and regions. These are the forces that will define the geography of future jobs and will shape the economic destiny of local communities around the world.

What are the main uncertainties for international financial markets? What causes a currency crisis? How do you deal with global imbalances? Understand the analytical tools used in the field of international macroeconomics and finance. Demonstrate how such tools can be applied by examining key policy issues of interest today.

What compensation should CEOs get? How can you motivate team performance? What impact does an altruistic manager make? Study real-world issues like policymaking, finance and management using economic models from contract theory, incentive theory, and the theory of the firm.

This module equips you with the tools commonly used in economics to critically assess the efficacy of current policies and the effects of policy changes. You will put theoretical knowledge into practice, closely reviewing canonical applications of these tools in the economics literature as well as performing evaluations yourself.

Get to grips with modern microeconometrics. Examine the nature of causal and noncausal models, discuss the nature of microeconomic data structures, and review core econometric methods in the first part of this module. Then, analyse limited dependent variable models, including discrete choice and selection models, and examine program evaluation methods.

How do you analyse stationary time series? Or non-stationary (integrated) processes? Understand the econometric methods available to analyse models of economic time series. Examine how methods of estimation and inference can be applied to these models. Learn how to use these methods in your own research.

What are the main issues with panel data? And the main econometric techniques to analyse panel data? What methods can you use to evaluate spell duration data? Answer such questions with examples from labour economics, while gaining the skills to analyse a variety of research and policy problems.

What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.