EC910-7-SP-CO:
Computational Economics
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2023/24
Economics
Colchester Campus
Spring
Postgraduate: Level 7
Inactive
Monday 15 January 2024
Friday 22 March 2024
20
23 October 2023
Requisites for this module
(none)
(none)
(none)
(none)
EC911
This module will introduce students to three topics in computational economics.
It will develop an agent-based computational economics model (ACE). It will then cover essential tools of modern labour economics, with a focus on search and matching models and their application to macroeconomic modelling. Finally, students will learn data analysis using large-scale data sets.
The aim of this module is:
- To provide first-step assistance to students in the development of core computational methods. These are agent-based models, search and matching models, and machine learning.
By the end of this module, students will be expected to be able to:
- Acquire the computational programming and modelling techniques necessary for modern macroeconomics and financial analysis.
Students will learn how to design, program, and implement an ACE model with financial networks.
Students will learn how these models can be used to address policy issues such as unemployment and wage inequality. The fundamental of estimation techniques will also be covered.
The "data science" developed here will focus on machine learning (ML) and how this technique can be applied to real-world economic and financial problems.
Classroom lectures will be accompanied by lab classes. All the computation will be done in R or Python.
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/.
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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 |
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 & Labs: Dr Ran Gu
For further information, send an email message to pgteco@essex.ac.uk.
No
No
No
Dr Domenico Moro
university of Birmingham
Lecturer
Available via Moodle
Of 40 hours, 20 (50%) hours available to students:
20 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Economics
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