Module manager: Kausik Chaudhuri
Email: K.Chaudhuri@lubs.leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
| LUBS2230 | Mathematics for Business and Economics 2 |
| LUBS2575 | Statistics and Econometrics |
| MATH0111 | Elementary Diff Calculus 1 |
| MATH0212 | Elementary Integral Calculus (Version 1) |
| MATH0370 | Introduction to Applied Mathematics 2 |
| MATH1000 | Core Mathematics |
| MATH1013 | Computational Mathematics and Modelling |
| MATH1331 | Linear Algebra with Applications |
| MATH2600 | Numerical Analysis |
| MATH2640 | Introduction to Optimisation |
| MATH2715 | Statistical Methods |
| MATH2740 | Environmental Statistics |
| MATH3723 | Statistical Theory |
| MATH3772 | Multivariate Analysis |
This module is not approved as a discovery module
This course provides students with mathematical tools to analyse dynamic economic and financial models. Students will learn to solve and interpret first- and second-order dynamic systems, construct diagrams, and apply these to topics such as economic growth, business cycles, and optimal consumption/portfolio choice. The emphasis is on economic intuition, stability analysis, and model applications rather than merely abstract mathematics.
Building on a foundation of mathematical economics and statistics, this module equips students with the analytical tools and the economic intuition required to evaluate complex dynamic models. By integrating the study of differential equations and an intertemporal mathematical framework, students will learn to model real-world phenomena such as economic growth, business cycles, and intertemporal choice. A core focus is placed on enabling students to study the stability and long-term behaviour of dynamic systems in both economic and financial contexts using appropriate diagrams and techniques.
The module is delivered through a blend of structured lectures and interactive seminars designed to bridge the gap between mathematical theory and practical economic insight. While lectures focus on the rigorous derivation of solutions and theoretical foundations, seminars provide a collaborative space for active learning and problem-solving. Through this dual approach, students will develop specialized skills in intertemporal optimization—applying these to consumption and investment decisions—while honing transferable strengths in quantitative reasoning and the communication of technical concepts.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
ALO1 - Understand and apply key mathematical techniques used in dynamic modelling.
ALO2 - Construct and interpret diagrams to analyse the stability and behaviour of dynamic systems.
ALO3 - Apply a mathematical framework to solve intertemporal optimisation problems in economics and finance.
ALO4 - Translate mathematical solutions into economic insights relevant to decision-making in organisations and the economy.
On successful completion of the module students will have demonstrated the following skills:
SLO1 - Problem solving and analytical skills
Through the transition from abstract equations to actionable insights, students build a logical rigour.
SLO2 - Systems thinking
Understanding mathematical models allows students to see how variables are connected and how each part of a model interacts with another.
SLO3 - Anticipatory/future thinking and Strategic Practice
Many of the models are intertemporal and therefore show how decisions today turn into outcomes tomorrow, which helps students move beyond short-termism.
SLO4 - Communication
Students will need to communicate the outcomes of models and be able to explain the complex models, method and outcomes.
Indicative syllabus
Dynamic methods are central to economics and finance as many important decisions and outcomes unfold over time. This course aims to introduce the core methods in analysing how economic and financial variables evolve dynamically rather than remaining static. The course starts with dynamic systems, which describe how variables interact and change across time. Two key concepts are introduced: difference equations, which model changes between discrete time periods (such as yearly or quarterly economic data), and differential equations, which describe continuous-time adjustments often used in financial modelling and economic growth theory. Phase diagrams are subsequently introduced as graphical way to visualise the behaviour of dynamic systems and analyse stability, equilibrium paths, and long-run outcomes. Finally, dynamic optimisation examines how individuals, firms, and policymakers make optimal decisions when present choices influence future outcomes, central to model investment, consumption, and financial portfolio decisions. In sum, the course provides a foundation for analysing intertemporal economic behaviour and the evolution of economic systems over time.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 13 | 1 | 13 |
| Seminar | 5 | 1 | 5 |
| Private study hours | 82 | ||
| Total Contact hours | 18 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Towards the end of the module students will have the chance to work in teams to receive feedback on a practice exam paper. Online MCQs will also be made available to students during the semester.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) (S1) | 2.0 Hrs Mins | 100 |
| Total percentage (Assessment Exams) | 100 | |
The resit for this module will be 100% by 2 hour examination.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team