also available in 2018
A Level Requirements
see all requirements
see all requirements
Full time 3 Year(s)
Taught by experts in departments that are internationally recognised for the quality of their research, studying Economics and Mathematics at Lancaster gives you the opportunity to develop the necessary knowledge and tools to understand how important Economics is to the functioning of government, business and society. At the same time you will acquire mathematical and analytical skills, which are much sought-after by many employers.
In your first year, you’ll take modules including Principles of Economics, Integration, and Matrix Methods before moving on to second-year subjects such as Linear Algebra, Probability, Intermediate Micro- and Macroeconomics, and Introductory Econometrics. You’ll complete your degree in your third year, following courses such as Likelihood Inference, Mathematical Economics, Monetary Economics and Advanced Microeconomic Theory.
A Level AAB
Required Subjects A level Mathematics or Further Mathematics grade A
GCSE Mathematics grade B or 6, English Language grade B or 6
IELTS 6.5 overall with at least 5.5 in each component. For other English language qualifications we accept, please see our English language requirements webpages.
International Baccalaureate 35 points overall with 16 points from the best 3 Higher Level subjects including 6 in HL Mathematics
BTEC May be considered alongside A level Mathematics and Further Mathematics with at least one at grade A
We welcome applications from students with a range of alternative UK and international qualifications, including combinations of qualification. Further guidance on admission to the University, including other qualifications that we accept, frequently asked questions and information on applying, can be found on our general admissions webpages.
Contact Admissions Team + 44 (0) 1524 592028 or via firstname.lastname@example.org
Many of Lancaster's degree programmes are flexible, offering students the opportunity to cover a wide selection of subject areas to complement their main specialism. You will be able to study a range of modules, some examples of which are listed below.
Students are provided with an understanding of functions, limits, and series, and knowledge of the basic techniques of differentiation and integration. Examples of functions and their graphs are presented, as are techniques for building new functions from old. Then the notion of a limit is considered along with the main tools of calculus and Taylor Series. Students will also learn how to add, multiply and divide polynomials, and will learn about rational functions and their partial fractions.
The exponential function is defined by means of a power series which is subsequently extended to the complex exponential function of an imaginary variable, so that students understand the connection between analysis, trigonometry and geometry. The trigonometric and hyperbolic functions are introduced in parallel with analogous power series so that students understand the role of functional identities. Such functional identities are later used to simplify integrals and to parametrise geometrical curves.
Each year students receive specific training by the Management School Career Team, to prepare them for the graduate labour market. In the first year the focus is on growing the student’s awareness of labour market dynamics and his or her professional aspirations and inclinations. The second year focuses on goal setting, action planning, and the development of a personalised career plan. The third year focuses on one-to-one sessions with career advisors. The Career Team is based in the Management School, organises events with employers and alumni, and coaches students on how to best perform in the graduate job market through seminars, surgeries, mock interviews and one-to-one advice.
This module extends the theory of calculus from functions of a single real variable to functions of two real variables. Students will learn more about the notions of differentiation and integration and how they extend from functions defined on a line to functions defined on the plane. They will see how partial derivatives can help to understand surfaces, while repeated integrals enable them to calculate volumes. The module will also investigate complex polynomials and use De Moivre’s theorem to calculate complex roots.
In mathematical models, it is common to use functions of several variables. For example, the speed of an airliner can depend upon the air pressure, temperature and wind direction. To study functions of several variables, rates of change are introduced with respect to several quantities. How to find maxima and minima will be explained. Applications include the method of least squares. Finally, various methods for solving differential equations of one variable will be investigated.
Introducing the theory of matrices together with some basic applications, students will learn essential techniques such as arithmetic rules, row operations and computation of determinants by expansion about a row or a column.
The second part of the module covers a notable range of applications of matrices, such as solving systems of simultaneous linear equations, linear transformations, characteristic eigenvectors and eigenvalues.
The student will learn how to express a linear transformation of the real euclidean space using a matrix, from which they will be able to determine whether it is singular or not and obtain its characteristic equation and eigenspaces.
Providing a thorough introduction to the discipline of Economics, this module is divided into two parts. The first part covers microeconomic analysis, including the theory of demand, costs and pricing under various forms of industrial organisation, and welfare economics. Many applications of theoretical models are examined. The second part focuses on macroeconomic analysis, including national income analysis, monetary theory, business cycles, inflation, unemployment, and the great macroeconomic debates.
Probability theory is the study of chance phenomena, the concepts of which are fundamental to the study of statistics. This module will introduce students to some simple combinatorics, set theory and the axioms of probability.
Students will become aware of the different probability models used to characterise the outcomes of experiments that involve a chance or random component. The module covers ideas associated with the axioms of probability, conditional probability, independence, discrete random variables and their distributions, expectation and probability models.
To enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems, the module begins with a brief overview of statistics in science and society. It then moves on to the selection of appropriate probability models to describe systematic and random variations of discrete and continuous real data sets. Students will learn to implement statistical techniques and to draw clear and informative conclusions.
The module will be supported by the statistical software package ‘R’, which forms the basis of weekly lab sessions. Students will develop a strategic understanding of statistics and the use of associated software, which will underpin the skills needed for all subsequent statistical modules of the degree.
Students will gain a solid understanding of computation and computer programming within the context of maths and statistics. This module expands on five key areas:
Under these headings, students will study a range of complex mathematical concepts, such as: data structures, fixed-point iteration, higher dimensions, first and second derivatives, non-parametric bootstraps, and modified Euler methods.
Throughout the module, students will gain an understanding of general programming and algorithms. They will develop a good level of IT skills and familiarity with computer tools that support mathematical computation.
Over the course of this module, students will have the opportunity to put their knowledge and skills into practice. Workshops, based in dedicated computing labs, allow them to gain relatable, practical experience of computational mathematics.
Students will be provided with the foundational results and language of linear algebra, which they will be able to build upon in the second half of Year Two, and the more specialised Year Three modules. This module will give students the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.
They will consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of their study will also involve looking at the concepts of length and angle with regard to vector spaces.
Probability provides the theoretical basis for statistics and is of interest in its own right.
Basic concepts from the first year probability module will be revisited and extended to these to encompass continuous random variables, with students investigating several important continuous probability distributions. Commonly used distributions are introduced and key properties proved, and examples from a variety of applications will be used to illustrate theoretical ideas.
Students will then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.
Statistics is the science of understanding patterns of population behaviour from data. In the module, this topic will be approached by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.
The focus will be on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and linear regression techniques within the statistical modelling framework will also be considered.
Students will come to recognise the role, and limitations, of the linear model for understanding, exploring and making inferences concerning the relationships between variables and making predictions.
This module helps you to explore the connections between economic theory and econometric and statistical methods as applied to issues of global significance.
The areas it covers include:
the economics of food security
the economics of crime, including cybercrime and the effects of deterrents and policing on crime
the transatlantic financial crisis: its causes, consequences and the future of macroeconomics poverty and inequality
This module focuses on the role of governments within the economy, looking at the extent to which they can intervene in markets and in other areas such as climate change. It builds your skills in evaluating the effectiveness of economic policies, and provides insights into the difficulties of decision-making in collective-choice environments.
Over the course of this module, you will enhance your knowledge and understanding of how to specify economic problems in the confines of a game-theoretic model and to solve those problems using appropriate mathematical techniques. The module aims to build your capacity for logical and structured problem analysis.
This module is designed to extend the knowledge of macroeconomics principles you acquired in Year 1.
classical and Keynesian views
the role of money
real balance and wealth effects
government budgetary constraints
monetary policy in the UK
models of exchange rate determination
Although the main focus of the module is on macroeconomic theory, this is taught within the context of current events in the international macroeconomic environment. You are encouraged to use your knowledge of macroeconomic theory to gain a better understanding of current macroeconomic events and issues.
This module emphasises the application of macroeconomic theory to current policy issues.
Considerable emphasis is placed on using analytical tools to gain a better understanding of the workings of the macroeconomy and the ways in which policy-makers respond to macroeconomic problems.
unemployment and inflation
adaptive and rational expectations
policy effectiveness under rational expectations
the economics of independent central banks
This module explores the decision-making of economic agents (consumers and firms), and also examines how different market mechanisms operate to allocate resources. The topics it covers include utility maximisation, profit maximisation, cost minimisation, and introduction to market structures.
The module requires algebra, elementary calculus, logical thinking and problem solving ability, and is normally taken in conjunction with Intermediate Microeconomics II (ECON221).
This module builds on learning gained in Intermediate Microeconomics 1 (ECON220), developing on the theories and concepts covered as well as focusing on a range of new topics.
analysis of monopoly behaviour and regulation
price and quantity setting in duopoly markets
introduction to game theory and strategic behaviour by firms
auctions (including a study of eBay)
general equilibrium and welfare economics
The module is normally taken in conjunction with ECON220.
This module provides an introduction to the theoretical concepts and applications of econometrics.
Econometric techniques taught include bivariate regression, multiple regression and two stage least squares. The importance and relevance of statistical and diagnostic testing is emphasised in the context of econometrics applications. You will also learn how to use the statistical package SPSS, understanding of which is an integral part of the module.
This module brings together economic theory and mathematical methods as a basis for constructing and using mathematical models to analyse economic problems.
The module covers matrix algebra, constrained optimisation, comparative statics and integral calculus. Knowledge of these methods will give you a broader understanding of intermediate and advanced microeconomics and macroeconomics.
Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.
This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.
Students will also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?
The approach taken in this module is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.
This module gives an overview of important topics in macroeconomics. Topics covered include the stabilisation policy under rational expectations, the Lucas critique of policy evaluation, and the implications of asset market efficiency for macroeconomic behaviour. The relevance of theory to these issues is emphasised throughout the module.
This module develops advanced topics in the field of microeconomic analysis, with an emphasis on formal mastery as well as intuitive interpretation and understanding.
This module develops your understanding of the application of macroeconomic theory and quantitative methods to the analysis of international economics and the economic history of the UK, and the pound sterling in particular. It also helps you to understand the role of international economics and finance in the world economy.
The module integrates intermediate macroeconomic theory, statistical methods, the interpretation of data, and empirical results. Analysis is applied to macroeconomic issues important to businesses and policymakers – including exchange rate regimes, international parity conditions, business cycles, and monetary unions.
Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.
Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.
This course aims to introduce students to the field of Behavioural and Experimental economics. The module provides the necessary skills to study how the standard rationality assumptions can be relaxed in order to take into account psychological and cognitive biases, as well as social preferences. In addition, it introduces students to the tool of experimentation in economics as a means of collecting data to test the various economic theories.
This module develops your understanding of advanced material in the field of economic growth as well as the problems of economic development. There is particular reference to the application of theoretical material to the development experience and policy-making in developing and emerging economies.
This module integrates intermediate economic theory, statistical methods and the interpretation of data to analyse applied economic issues of current business or policy importance.
You will be prepared for your 5,000-word dissertation on an applied topic by attending workshops either individually or in groups. The workshops are supervised by a member of staff.
This module equips you with the tools needed to conduct applied econometrics. It emphasises an analytical and intuitive understanding of the classical linear regression model, and also covers newer topics such as:
binary choice models
non-stationary time series
unit root tests and basic cointegration
You will use the Eviews econometrics software.
This applied module is designed as an introduction to the economics of health and health care, and helps to develop your awareness of the main policy issues in this field. It provides a comprehensive set of economic tools for critically appraising fundamental issues in the economics of health while offering a broad overview of the UK National Health Service and other health care systems around the world. The emphasis is on the use and interpretation of microeconomic models and the latest empirical evidence.
This module focuses on firm behaviour and competition, using theoretical (especially game theoretic) and empirical models. It also explores the relationship between industry structure and firm conduct, together with aspects of firm behaviour such as advertising, R&D and mergers.
This module develops your understanding of advanced material in the field of international business. There is particular emphasis on analysing the strategic economic and financial behaviour of multinational firms in the global economy. The module also considers the role and effects of government intervention on firms and multinational firms and how they adapt.
This module develops your understanding of concepts and theories of international trade and factor flows, with particular reference to the way in which such material can inform policy-making.
You will gain knowledge and understanding of international trade theory, and learn to apply this theory to the analysis of present-day policy issues in international economics.
Focusing on the microeconomics of labour and personnel, this module covers topics such as the economics of migration, wage determination, job search and labour market discrimination.
There is a particular emphasis on principal agent problems in human resources and the design of incentives within firms.
Economics theory is used to analyse the operation of labour markers and assess the empirical evidence. Areas covered include:
This module trains ambitious economists to use formal mathematical methods used in economic modelling. These techniques are necessary for students interested in pursuing postgraduate studies in economics or working in analytically demanding jobs in the private sector. You will learn to use the Mathematica software package and how to address and solve economic problems by means of abstract models.
The first part of the module is more micro-oriented and you will learn further methods of integration and what metric spaces and existence theorems are. You will also learn what quasi-concave and quasi-convex functions are and how to optimise with inequality constraints. The second part of the course is more macro-oriented and you will learn how to solve differential equations and perform dynamic optimisation.
The aim is to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, causality and confounding.
Students will develop a firm understanding of the key analytical methods and procedures used in studies of disease aetiology, appreciate the effect of censoring in the statistical analyses, and use appropriate statistical techniques for time to event data.
They will look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems they are investigating as well as the mathematical and statistical concepts underpinning inference.
This module examines the essential characteristics of a money economy, and the topics covered include:
Interest rate determination
Monetary and labour market disequilibria
The national debt and monetary disequilibrium (fiscal monetarism)
Monetary and international payments disequilibria
Rational expectations (RE) solutions and applications to stabilisation policy
Market efficiency and the application of RE to bubbles and to exchange rates
The central bank and inflation bias
This module is concerned with understanding the role of government in the economy. Among the topics covered are the characteristics of public goods, basic characteristics of a tax system under both classical and political economy approaches, and the effects of globalisation and its effects on modern tax systems. The module also explores how economic models can be used to understand current affairs.
This module looks at how theoretical and empirical methods can be applied to the growing field of sports economics.
It helps you to understand the particular characteristics of labour and product markets in professional sports, and what implications these have for economic analysis. You will also learn more about how theoretical and empirical work in the economics of sport can be used to inform policy issues, including competitive structures in sports leagues, free agency and player mobility, and the financing of professional sport. You will also gain insights into how betting markets function and why people gamble on sports.
The concept of generalised linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables, will be explored. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal).
Students will come to understand the effect of censoring in the statistical analyses and will use appropriate statistical techniques for lifetime data. They will also become familiar with the programme R, which they will have the opportunity to use in weekly workshops.
Important examples of stochastic processes, and how these processes can be analysed, will be the focus of this module.
As an introduction to stochastic processes, students will look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).
The focus will then be on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). Students will discover how to analyse Markov processes, and how they are used to model queues and populations.
Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module students will become familiar with topics from classical statistics as well as some from emerging areas.
Time series data will be explored through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. Time series and volatility modelling will also be studied, and the techniques for the analysis of such data will be discussed, with emphasis on financial application.
Another area the module will focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis.
Lastly,students will spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.
Lancaster University offers a range of programmes, some of which follow a structured study programme, and others which offer the chance for you to devise a more flexible programme. We divide academic study into two sections - Part 1 (Year 1) and Part 2 (Year 2, 3 and sometimes 4). For most programmes Part 1 requires you to study 120 credits spread over at least three modules which, depending upon your programme, will be drawn from one, two or three different academic subjects. A higher degree of specialisation then develops in subsequent years. For more information about our teaching methods at Lancaster visit our Teaching and Learning section.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
As a graduate of Lancaster you’ll enjoy excellent employment prospects. Your qualification in Economics and Mathematics, along with your problem-solving skills, analytical abilities and organisational expertise, will make you highly desirable to employers.
Former graduates have been taken on as professional economists and economic advisers by the Bank of England, the Civil Service, management consultancies and diverse companies in a wide range of areas.
Your skills are also easily transferable to various roles such as marketing, management, advertising and consultancy.
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with the relevant life and work based skills. We are unique in that every student is eligible to participate in The Lancaster Award which offers you the opportunity to complete key activities such as work experience, employability awareness, career development, campus community and social development. Visit our Employability section for full details.
Lancaster Management School has an award winning careers team to provide a dedicated careers and placement service offering a range of innovative services for management school students. Our high reputation means we attract a wide range of leading global employers to campus offering you the opportunity to interact with graduate recruiters from day 1 of your degree.
We set our fees on an annual basis and the 2019/20 entry fees have not yet been set.
As a guide, our fees in 2018 were:
Some science and medicine courses have higher fees for students from
the Channel Islands and the Isle of Man. You can find more details here:
For full details of the University's financial support packages including eligibility criteria, please visit our fees and funding page
Students may incur travel costs dependant on their placement location.
Students also need to consider further costs which may include books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation it may be necessary to take out subscriptions to professional bodies and to buy business attire for job interviews.
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Typical time in lectures, seminars and similar per week during term time
Average assessment by coursework