ECONOMIC ANALYSIS WITH MATRICES - 2026/7

Module Overview

The module is composed of three main areas: (1) an introduction to linear algebra; (2) an introduction to programming in MATLAB; and (3) applications of (1) and (2) to finance and economics problems. In the first part of the course, students develop an understanding of key concepts in linear algebra (vectors, matrices, rank, determinants, system solutions, and quadratic forms), while also learning the functionality of MATLAB, its syntax, and the use of AI tools to support code generation and debugging. In the second part of the course, students apply the toolkit developed in the first part to real-world finance and economics scenarios, with an emphasis on critically evaluating, validating, and refining AI-assisted coding.

Module provider

Economics

Module Leader

RISPOLI Luciano (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

 Indicative content includes:

• Matrix algebra: vectors and matrices; matrix operations: addition, subtraction, multiplication, inversion; notation, linear dependence, determinants, rank, solution to a set of linear equations and quadratic forms
• Representation of simple economic models in matrix form
• Introduction to programming and debugging in MATLAB, including the effective and critical use of AI tools for code generation, validation, and error detection
• Fetching economic data through MATLAB
• OLS model in matrix form
• IS-LM model solution in matrix form
• Mechanical Trading rules 

Assessment pattern

Alternative Assessment

For both the group presentation and final group project, alternative assessments will be an individual presentation and project assignment in the appropraite reassessment period. 

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of basic matrix algebra, its implementation in standard software packages (i.e. MATLAB), and its application to selected real-world socio-economic problems, while also fostering critical engagement with AI-assisted coding practices, including the validation and justification of generated approaches.

Thus, the summative assessment for this module consists of:

• Group presentation aimed at evaluating the approaches and steps undertaken towards the completion of the final group project, including the critical use of AI tools for code generation and debugging. The assessment will test students' ability to explain, validate, and justify AI-supported solutions, alongside their knowledge of MATLAB programming and presentation skills, and is aligned with Learning Outcomes 2, 3, 4, and 5.
• Final group project which allows students to analyse a topic in depth by using techniques and concepts assimilated during the module. Each project will consist of MATLAB code, a written report, and an assessment of individual performances in the group. The assessment is designed to evaluate skills in MATLAB programming, including the effective and critical use of AI tools for code generation and debugging, the ability to gather, analyse and interpret information on a particular topic, and to use this knowledge to produce a written report. Students will also be required to validate and justify any AI-supported solutions. The project further assesses the ability to work in a group and evaluate individual performances within the team (Note: individual marks within a group are capped at the overall project mark; students with lower contribution weights will be adjusted accordingly). This assessment is linked to all learning outcomes (1, 2, 3, 4 & 5).

 Formative assessment and Feedback:

Students receive verbal feedback during lectures and computer labs (including feedback on safe and responsible use of AI tools). Furthermore, self-paced tests will be available to give the chance to students to test their knowledge throughout the semester. Videos may also be used to deliver solutions of assignments, explaining concepts and procedures involved when the proposed solution code requires further clarifications. Computer lab feedback sessions deliver extensive formative assessment and feedback, possibly in small groups.

Module aims

• Provide students with an appreciation of the variety of applications of simple matrix algebra in economics, finance and other social sciences.
• Promote students' confidence in applying and creating data-driven models solving real word problems with the use of standard software packages (i.e. MATLAB).
• Help students to build a basic understanding of programming techniques.
• Help students develop an appreciation and relevance of programming techniques within the "professional economist toolkit".
• Provide students with the relevant software and independent research skills that will enable them to successfully undertake the writing of the economics project (ECO3050).
• Develop students' ability to effectively and critically use AI tools for code generation and debugging, with an emphasis on reasoning, validation, and the responsible evaluation of generated solutions.

Learning outcomes

Methods of Teaching / Learning

  The learning and teaching strategy is designed to:

• develop skills in both data analysis and written/oral presentation
• develop appreciation of the variety of applications of matrices in economics and finance
• develop understanding of the use of standard software packages (i.e. MATLAB) in the analysis of economic, financial data and models
• develop understanding of basic programming techniques, including the effective and critical use of AI tools for code generation, debugging, and validation

The learning and teaching methods include:

• Lectures aimed at introducing the relevant theory and topics as well as at applying the relevant theory to real-world examples and scenarios
• Computer lab sessions aimed at supporting students' learning and application of key concepts, including the effective and critical use of AI tools for coding and debugging.
• Captured content video-tutorials with practical exercises aimed at introducing students to MATLAB programming
• Guided learning aimed at providing students with references in support to the material seen in class

Students are expected to actively engage throughout the course. More specifically, they are expected to contribute to lectures and tutorials, critically reflect on their use of computational and AI tools, and validate the outputs they produce. Students are also expected to bring their laptops in order to practise their coding skills during lectures and tutorials.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECO2048

Other information

The School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities particularly in the following areas:

Digital capabilities

Students will develop the capacity to manage information and databases pertaining to various types of data. The module also requires students to use a software (MATLAB) to showcase familiarity with a wide range of statistical and descriptive techniques. These activities have the ultimate effect of leveraging students’ digital skills.

Employability

Students are equipped with theoretical and practical problem-solving skills, and transferable mathematical and theoretical knowledge that will allow them to analyze in theory and in practice data driven financial and economic applications. All of this is highly valuable to employers for different roles: e.g. economists, financial traders and investment analysts.

Global and Cultural Capabilities  

Students learn to work together in groups with other students from different backgrounds to solve assignments. This module allows students to develop skills that will allow them to effectively collaborate with individuals from around the world.

Resourcefulness and Resilience

Final group assessment features real-world problem-based tasks. This final group project also fosters the development of team working, confidence and professionalism.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.