Economics and Mathematics (Industry) BSc Hons - 2021 Entry

Course Structure

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 to complement your main specialism. 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 please visit our Teaching and Learning section.

The following courses do not offer modules outside of the subject area due to the structured nature of the programmes: Architecture, Law, Physics, Engineering, Medicine, Sports and Exercise Science, Biochemistry, Biology, Biomedicine and Biomedical Science.

Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes and new research.

Year1

Core

• Calculus

  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.

• Career and Employability I

  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.

• Further Calculus

  This course extends ideas of MATH101 from functions of a single real variable to functions of two real variables. The notions of differentiation and integration are extended from functions defined on a line to functions defined on the plane. Partial derivatives help us to understand surfaces, while repeated integrals enable us to calculate volumes.

  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 and temperature, and the direction of the wind. To study functions of several variables, we introduce rates of change with respect to several quantities. We learn how to find maxima and minima. Applications include the method of least squares. Finally, we investigate various methods for solving differential equations of one variable.

• Linear Algebra

  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.

• Principles of Economics A

  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

  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.

• Statistics

  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.

Year2

Core

• Career and Employability II

  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.

• Computational Mathematics

  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:

  • Programming and R
  • Numerical solutions of equations
  • Numerical differentiation and integration
  • Monte Carlo methods
  • Numerical solutions to ODEs

  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.

• Linear Algebra II

  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 II

  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 II

  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.

Year3

Core

• Management School UG Industrial Placement

  In your first year you'll apply for placements in business or industry, and will take a module to prepare you for the placement year. The whole of your third year will then be spent working in a paid placement, supported by the Careers team. Near the end of the placement, you'll submit a proposal for your dissertation topic, inspired by your experiences during the placement year, which you'll complete in fourth year under the supervision of an academic tutor.

Year4

Core

• Business Placement Dissertation

  This module equips students with experience of working within a business environment. You are expected to acquire not only knowledge of business problems and practices, but also experience of interpersonal relationships within a business context. The dissertation is undertaken during the work placement year.

• Career and Employability III

  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.

• Likelihood Inference

  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 course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.

Optional

• Advanced Macroeconomics

  The course introduces state-of-the-art methods in macroeconomic analysis with a particular focus upon issues that matter for economic stabilization. Students will develop a broad and deep knowledge of modern macroeconomic models that require the use of advanced analytical methods. In particular, we will develop and understand the workings of the Neoclassical Growth model, the Real Business Cycle (RBC) model and the New Keynesian (NK) model. We will use these frameworks to examine the role of governments and central banks, as well as contemporary issues such as restrictions imposed by the zero lower bound, and the effectiveness of monetary and fiscal policies in a liquidity trap. In the context of the New Keynesian model, we will also investigate optimal stabilization policies along the business cycle, including the role of unconventional fiscal and monetary policies such as temporary tax measures, forward guidance and negative interest rates. The emphasis of the module is on the application of theory to these issues. At a more hands-on level, students will also be introduced to methods of computer programming that are used to solve macroeconomic models, giving them an additional skillset to apply their theoretical understanding. ECON 324 provides a solid foundation for a macroeconomics dissertation, a postgraduate degree in Economics, and advanced research projects in academia and central banks.

• Advanced Microeconomics

  This module develops advanced topics in the field of microeconomic analysis, with an emphasis on formal mastery as well as intuitive interpretation and understanding.

  Topics include:

  • duality and the envelope theorem
  • the Robinson Crusoe economy
  • prices and decentralisation
  • general equilibrium
  • the core decision-making under risk and uncertainty
  • game theory (correlated equilibrium)
  • information and mechanism design
  • the equilibrium price of time
• Applied Economics

  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

• Bayesian Inference

  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.

• Behavioural and Experimental Economics

  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.

• Development Economics

  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.

• Econometrics

  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:

  • specification tests

  • binary choice models

  • Tobit analysis

  • non-stationary time series

  • unit root tests and basic cointegration

  You will use the Eviews econometrics software.

• Game Theory

  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.

• Generalised Linear Models

  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.

• Health Economics

  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.

• Industrial Organisation

  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.

• International Trade

  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.

• Labour 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:

  • the firm's demand for labour
  • labour supply at an individual and aggregate level
  • causes of wage rigidity
  • the economic impact of trade unions
  • inflation and unemployment
• Mathematical Economics

  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.

• Medical Statistics: study design and data analysis

  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.

• Monetary Macroeconomics

  This module examines the essential characteristics of a money economy, and the topics covered include:

  • Non-neutral money

  • 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

• Public Economics

  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.

• Stochastic Processes

  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.

• Time series analysis

  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.