Module code: ECO3063

Module Overview

This module introduces students to computational methods for solving and simulating economic models. The student will learn basic results and techniques in numerical analysis, acquire a working knowledge of programming language, and learn how to apply these tools to analyse quantitative implications of macroeconomic dynamic models and macroeconometrics. Matlab will be the main language taught in the course, while learning other languages (e.g. Python, Julia, etc) will be optional.

Module provider


Module Leader

TSUJIYAMA Hitoshi (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

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

Overall student workload

Independent Learning Hours: 63

Lecture Hours: 22

Laboratory Hours: 5

Guided Learning: 38

Captured Content: 22

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

-Command the basics of programming language
-Numerical analysis
-Basic dynamic macroeconomic models
-Macroeconomic policy analysis with computational modeling and coding

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 1 50
Coursework COURSEWORK 2 50

Alternative Assessment

Different individual coursework assignments will be given for students who are resitting.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate: Familiarity and understanding of the theoretical notions of numerical methods as well as being able to implement quantitative analysis through programming language of real world macroeconomic policy.

Thus, the summative assessment for this module consists of:

  • a first coursework 

  • a final coursework

Formative assessment & feedback

Students receive verbal feedback during the in-person lectures and tutorial sessions. Students are provided with a set of exercises relating to the lecture material which they solve independently or in teams in preparation for the tutorials. In these seminar tutorials, they receive feedback on their answers, and guidance on how these answers could be improved. In addition to this, students receive guideline solutions online. Office hours provide students with further feedback and consultation opportunities.

Module aims

  • Appreciation of the relevance of computational analysis in economics
  • Understand basic techniques for macroeconomic modelling
  • Be able to write intuitive algorithms and clear computer code

Learning outcomes

Attributes Developed
001 Understanding numerical methods for economic analysis CKP
002 Establish a computational strategy to solve an economic model CKP
003 Investigate the impact of macroeconomics policy with quantitative analysis CKP
004 Develop effective teamwork on a common project CKPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to: Explore the theoretical concepts of numerical methods and familiarize with computation coding. Be able to apply the computational technique learned in the lectures to macroeconomic policy. Equip students with the necessary knowledge and skills to enable them to conduct independent research in the field of quantitative economics. The learning and teaching methods include:

- Lectures
- 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
Upon accessing the reading list, please search for the module using the module code: ECO3063

Other information

This module incorporates some of the University's five pillars of learning as follows:

Resourcefulness and resilience: Students in this module will develop their understanding and skills through assessment and feedback in tutorials.

Employability: This module will provide students with relevant soft skills to prepare them to successfully compete in the labour market and continuing education.

Digital Capabilities: This module will provide students with relevant programming skills to carry out quantitative work.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Economics and Data Analytics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Business Economics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Economics and Finance BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Economics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Economics and Mathematics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module

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 2025/6 academic year.