EC910-7-AU-CO:
Computational Economics

PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
The details
2025/26
Economics
Colchester Campus
Autumn
Postgraduate: Level 7
Inactive
Thursday 02 October 2025
Friday 12 December 2025
20
14 October 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

EC911

Key module for

(none)

Module description

This module is your gateway to the exciting intersection of economics and technology. Designed for students with no prior programming experience, it offers a comprehensive introduction to three key areas:



  1. Fundamentals of R Programming: Unlock the power of R programming through hands-on instruction and practical exercises. From basic syntax to advanced data manipulation, you'll gain the skills needed to harness the full potential of this versatile language.

  2. Agent-Based Computational Economics (ACE) Modelling: Delve into the realm of ACE modelling, where you'll learn to construct and simulate intricate financial networks to gain insights into dynamic economic systems.

  3. Machine Learning Applications: Harness the power of machine learning algorithms to tackle real-world economic and financial challenges, equipping yourself with invaluable tools for predictive modelling and data-driven decision-making.


Through engaging lectures and interactive laboratory sessions, you'll gain practical experience in leveraging computational techniques to tackle real-world economic problems. Join us and embark on your journey to becoming a tech-savvy economist!

Module aims

The aim of this module is:



  • To empower students with the computational skills and analytical techniques needed to excel in modern economic and financial analysis.

Module learning outcomes

By the end of this module, students will be expected to be able to have:



  • Developed a strong command of R programming fundamentals.

  • Acquired a deep understanding of ACE modelling and its implications for financial network analysis.

  • Gained practical experience in applying machine learning techniques to solve real-world economic and financial problems.

Module information

No additional information available.

Learning and teaching methods

This module will be delivered via:

  • 10x 2-hour lectures and 10x 2-hour laboratory sessions, immersing yourself in a dynamic learning environment.

Engage with expert instructors, collaborate with peers, and unlock your full potential in Computational Economics.

Bibliography*

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Ran Gu, email: ran.gu@essex.ac.uk.
Lectures and Labs: Dr Ran Gu
For further information, send an email message to pgteco@essex.ac.uk.

 

Availability
No
No
No

External examiner

Dr Domenico Moro
university of Birmingham
Lecturer
Resources
Available via Moodle
Of 4 hours, 4 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information
Economics

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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