EC910-7-AU-CO:
Computational Economics
2023/24
Economics
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
20
23 October 2023
Requisites for this module
(none)
(none)
(none)
(none)
EC911
This module assumes no prior programming experience.
This module will introduce students to some useful R and Python programming techniques. It will then develop an agent-based computational economics model (ACE). Students will learn how to program and implement an ACE model of financial networks. Finally, students will learn machine learning and how to apply this technique to real-world economic and financial problems.
The aim of this module is:
- To assist students in developing basic computational skills.
By the end of this module, students will be expected to be able to:
- Acquire the computational techniques necessary for modern economic and financial analysis.
No additional information available.
This module will be delivered via:
- One 2-hour lecture per week.
- One 2-hour lab and class per week.
-
Irizarry, R.A. (2019)
Introduction to Data Science: data analysis and prediction algorithms with R. Boca Raton: CRC Press. Available at:
http://rafalab.dfci.harvard.edu/dsbook-part-1/.
-
Irizarry, R.A. (no date)
Advanced Data Science. Available at:
http://rafalab.dfci.harvard.edu/dsbook-part-2/.
-
QuantEcon DataScience (no date). Available at:
https://datascience.quantecon.org/.
-
Lengnick, M. (2013) ‘Agent-based macroeconomics: A baseline model’,
Journal of Economic Behavior & Organization, 86, pp. 102–120. Available at:
https://doi.org/10.1016/j.jebo.2012.12.021.
-
Burdett, K. and Mortensen, D.T. (no date) ‘Wage Differentials, Employer Size, and Unemployment’,
International Economic Review, 39(2), pp. 257–273. Available at:
https://doi.org/10.2307/2527292.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Participation |
|
5% |
| Coursework |
Term Paper |
|
95% |
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
Reassessment
Module supervisor and teaching staff
Dr Ran Gu, email: ran.gu@essex.ac.uk.
Lectures and Labs: Dr Ran Gu
For further information, send a message to rg18762@essex.ac.uk
No
No
No
Dr Domenico Moro
university of Birmingham
Lecturer
Available via Moodle
Of 40 hours, 38 (95%) hours available to students:
2 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
Economics
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