抖阴APP导航

Data science is transforming the role of information technology in society and in many sectors in which economists work. Machine learning and big data methods have gained popularity as tools in academic, government, industry, and beyond.

You will be introduced to big data and machine learning techniques with a focus on economic applications. These techniques are already significantly impacting the field of economics for modelling economic relationship, drawing causal inferences, and making predictions. They will soon become a standard toolbox for economists.

Statistical techniques are often applied to environmental data, such as air temperatures, rainfall or wildfire locations. You will learn about some of the common features of such datasets and how these features are used to design statistical models. You will first be introduced to the Gaussian process model for continuous spatial processes. You will learn about the properties of the Gaussian process and implement this model for spatial data analysis, before investigating methods for point-reference data, such as earthquake or wildfire locations.

You will also dip into natural hazard risk management, which seeks to mitigate the effects of events, such as flooding or storms, in a manner that is proportionate to the risk. You will learn basic concepts from extreme value theory, including the appropriate distributions for extremes, and how to use these as statistical models for estimating the probability of events more extreme than those in the dataset.

This module will introduce you to current economic research. Experts from a variety of fields will teach you in-depth about how economic research is conducted and its real-world applications. By the end of this module, you will be able to:

• Apply advanced economic methods and concepts to evaluate contemporary economic problems.
• Critically think about the issues raised by economic research, including their applications and limitations.
• Present the methods and conclusions of advanced economic research using a wide variety of presentational tools.
• Consolidate the core knowledge, methods, and analytical skills you have developed throughout your degree.

These skills will prepare you for a professional career in economics and related fields, as well as for further academic study.

This module builds on the foundations of monetary and fiscal policy analysis by placing policy decisions in a global context. The first part of the module emphasises the interpretation and analysis of macroeconomic data. You will learn how to apply empirical methods to understand fluctuations in output, employment, inflation, and trade balances.

The second part focuses on the design and coordination of monetary and fiscal policy in an interconnected world. Special attention will be given to the challenges that central banks and governments face in managing global shocks. The topics covered will include international policy spillovers, exchange rate regimes, capital flows, and the evolving role of institutions such as the IMF and the Bank of England. We will explore real-world applications and current policy debates throughout the module.

The study of graphs (mathematical objects used to model networks and pairwise relations between objects) is a cornerstone of discrete mathematics. Graphs can represent important real-world situations, and the study of algorithms for graph-theoretical problems has strong practical significance.

You will learn about structural and topological properties of graphs, including graph minors, planarity and colouring. We will introduce several theoretical tools, including matrices relating to graphs and the Tutte polynomial. We will also study fundamental algorithms for network exploration, routing and flows, with applications to the theory of connectivity and trees, considering implementation, proofs of correctness and efficiency of algorithms.

You will gain experience in following and constructing mathematical proofs, correctly and coherently using mathematical notation, and choosing and carrying out appropriate algorithms to solve problems. The module will enable you to develop an appreciation for a range of discrete mathematical techniques.

This module provides a comprehensive exploration of international trade and global business dynamics, connecting theoretical models with practical policy implications. You will examine core trade theories including the Ricardian model, Heckscher-Ohlin model, and heterogeneous firm models. The module also offers in-depth analyses of international factor mobility, trade policies, and globalisation trends.

On the international business side, you will study key topics such as global value chains, multinational firm strategies, international competitive advantage, and the economic impacts of outsourcing and offshoring. The module focuses on real-world applications, exploring how theoretical frameworks inform understanding of contemporary economic phenomena, including labour productivity, attitudes towards trade, the effects of immigration, and the evolving landscape of global economic interactions.

Mathematical models are central to financial decision making. You will discover the mathematical foundations necessary to model certain transactions in the world of finance. You will then study stochastic models for financial markets and investigate the pricing of European and American options and other financial products.

You will explore two discrete models, the binomial model and the finite market model, and one continuous model. Following an introduction to some probabilistic terminology, such as sigma algebras and martingales, and some financial terminology such as arbitrage opportunities and self-financing trading strategies, you will deduce the Black Scholes formula. You will also gain a brief overview of Brownian motion.

From denoising diffusion to flow matching, modern generative models are governed by elegant mathematics: stochastic differential equations, PDEs for probability evolution and transport on spaces of measures. This module develops that mathematical toolkit and shows how it underpins today’s state-of-the-art image, audio and scientific generative models.?

We start from how probability distributions evolve over time (continuity and Fokker–Planck equations) and show how this leads to a reverse-time stochastic differential equation and an equivalent probability-flow ODE. We then look at discrete-time diffusion models and explain why their training objective is a practical stand-in for maximum likelihood estimation.

You will be introduced to score matching and denoising score matching, continuous-time formulations and the main numerical solvers (e.g. Euler–Maruyama, predictor–corrector), together with sensible choices of noise/step-size schedules. In parallel, we cover flow-based models: continuous normalising flows and flow matching, which fit a velocity field along a path between distributions, with links to optimal transport and Schrödinger bridges. We finish with fast samplers (e.g., distilled/consistency models) and with how to judge models in practice - negative log-likelihood, bits per dimension and coverage - while balancing against compute cost, stability and common failure modes.?

By the end, you will be able to read and reproduce the derivations that make these models work, implement small-scale prototypes, and reason from first principles about design choices such as noise schedules, guidance and solver accuracy.??

Public policy analysis is the study of government’s role in the economy. It involves examining both its normative and positive aspects. To gain a comprehensive understanding, we look at a combination of theories, empirical findings, and real-world examples.

We begin by focusing on public goods such as water, transportation, and other infrastructure that the government can provide directly or in collaboration with the private sector. This includes looking at the practice of regulators, as well as cost-benefit analysis. We evaluate the trade-off between efficiency and fairness, then examine state financing, including theories of optimal taxation and recent research on tax evasion and avoidance. Finally, we delve into the internal structure of government, exploring political economy and fiscal federalism.

Statistics and machine learning share the goal of extracting patterns or trends from very large and complex datasets. These patterns are used to forecast or predict future behaviour or interpolate missing information. Learn about the similarities and differences between statistical inference and machine learning algorithms for supervised learning and how the two approaches can be used for classification and prediction.

You will explore the class of generalised linear models, which is one of the most frequently used classes of supervised learning model. You will learn how to implement these models, how to interpret their output and how to check whether the model is an accurate representation of your dataset. Lastly, you will have the opportunity to see how regularisation and dimension-reduction techniques can be used to apply these models to the case of the ‘large p, small n’ question. This phrase refers to datasets with many more variables than samples.

Stochastic processes are fundamental to probability theory and statistics and appear in many places in both theory and practice. For example, they are used in finance to model stock prices and interest rates, in biology to model population dynamics and the spread of disease, and in physics to describe the motion of particles.

During this module, you will focus on the most basic stochastic processes and how they can be analysed, starting with the simple random walk. Based on a model of how a gambler's fortune changes over time, it questions whether there are betting strategies that gamblers can use to guarantee a win. We will focus on Markov processes, which are natural generalisations of the simple random walk, and the most important class of stochastic processes. You will discover how to analyse Markov processes and how they are used to model queues and populations.