# About the Programme

The MSc in Mathematical Economics and Econometrics (MEE) is open to students with a strong quantitative background, whether it is in economics, or in subjects such as mathematics, physics, engineering and computer science.

Postgraduate study and research in economics often builds on quantitative techniques, such as computer programming and proofs in the style of pure mathematics. If you have a strong quantitative background you’re likely to have a head start in studying advanced economics - you may even bring important techniques to the economics profession. Our MSc Mathematical Economics and Econometrics will build on your existing quantitative skills and prepare you for a career as a professional economist or for study in top economics PhD programmes.

## Why are quantitative methods so important in economics?

University-level mathematics is a prerequisite for many Masters and PhD programmes. See for example, the American Economics Association’s webpage about Math Preparation for Graduate School.

Economists use quantitative methods in a few ways:

- Mathematical proofs are used to verify the logic in economic reasoning. For example, Akerlof’s (1970) paper, “The Market for Lemons: Quality Uncertainty and the Market Mechanism” illustrates how markets can collapse when the seller knows more than the buyer (or vice versa). His model has been critical for understanding the failures of private health insurance markets. But Akerlof illustrates the logic in the context of the market for used cars. The logic is about a vicious circle. Sellers who know they have a good car will insist on a high price, whereas those who know they have a “lemon” will be happy to sell cheap. If the buyer can not tell the difference (until it is too late), then he will only be willing to pay a price in the middle. But this means no good cars would be sold, so in fact only lemons would trade. Stories like this need to be checked for logical fallacies such as circular reasoning.
- Analysis in infinite dimensions is used in almost all macroeconomic models, since a major part of economic activity is preparing for the future. Since the future never ends, realistic models of macroeconomics require an infinite number of time periods, and hence an infinite number of choices (how much to study, how many houses to build, and so on, for all of eternity). Navigating these infinities require careful mathematical reasoning.
- Statistical modelling is used to compensate for the fact that most important economic questions cannot be answered by running randomized controlled trials. (Imagine a government trying to run a random foreign policy!) This is a problem, because we would like to measure the consequences of economic policies. For example, do university education subsidies open new doors, or just give free money to students who would have been successful anyway? To answer this type of question, econometric models must capture not just the obvious processes that we directly see in the data (e.g. the correlation between years of education and wages), but also the processes hidden from us that might confuse our estimates (e.g. how a student’s hidden talents affects their chances of a scholarship award).
- Computer programming is used to calculate model predictions, and to calibrate models to resemble markets of interest. Economic models typically have two mathematical ingredients:
- Optimisation problems which capture how people’s choices respond to changes in their circumstances, such as changes in prices or changes in government policies, and
- Fixed point problems which capture the idea that when many people make decisions, their choices must cohere in the sense that nobody would benefit from changing their mind. (Otherwise, somebody would change their mind and make a different choice.)

- Both types of problem can be solved numerically. There is no single algorithm that applies to every model. The economist must piece together standard algorithms or devise new ones to calculate model predictions.

Note: while the core coursework involves only minimal and specialised computer programming, many students use programming in their dissertations.

## What is studying postgraduate Economics like?

The degree is very intensive – students often report that they study for 60 hours per week. From September until February, the courses are mostly theoretical in nature – they are more about tools than economic policy questions. For example, topics include equilibrium in Bayesian games and search theory models of unemployment. In addition to learning about tools and classical models, students naturally think about what makes a good economic model? Are our models too complicated with too much maths? Are our models too simple to be “realistic”? If you are curious, we recommend:

- Lucas (1988),"What Economists Do" (PDF) (Plain Text)
- Rubinstein (2006), "Dilemmas of an economic theorist" (PDF) (Plain Text) . Also see his book (Sign up required) - “Economic Fables”

From March onwards, when students begin their dissertation or research project, and take the topics courses, the focus switches. Each student picks a narrow economic issue or policy question to study, such as “what is the best way to run an IPO auction?”, or “how should the inflation rate of crypto currencies be set?” or “did Airbnb lead to higher housing rents?” The tools become a means to an end.

## What if I have never studied economics before?

The programme is ideal for you. It emphasises core principles in economics and econometrics. The programme shares a number of courses with our existing MSc in Economics, but is tailored for people with quantitative skills, whether or not they studied economics before. There is no question that this programme is intensive, and you will face a steep learning curve. Students who only studied economics in their undergraduate degrees will be working hard to develop their skills in mathematics and computing techniques they will need. In contrast, students with strong quantitative science backgrounds will be working to develop their economic intuition and skills to adapt their quantitative skills to economic situations. Either way, graduates with strong quantitative skills are highly sought after.

## What careers can graduates pursue?

Graduates from postgraduate economics programmes often work in international agencies, central banks, government economic services, consulting, finance, or enter leading international PhD programmes.