EC915-7-SP-CO:
Data Science for Economics
2023/24
Economics
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
20
15 January 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
This postgraduate module equips students with the key tools in modern data science, with a focus on machine learning (ML) and its application to Economics and Finance.
The aim of this module is:
- To enable students to understand how machine learning tools can complement the tools of traditional econometrics and how to apply these techniques to real-world economics and finance problems.
By the end of this module, students will be expected to be able to:
- Develop a comprehensive understanding of key concepts in modern machine learning (ML): classification, prediction, supervised and unsupervised learning.
- Demonstrate a critical understanding of the advantages and disadvantages of ML as compared to traditional econometric approaches.
- Apply ML to real-world economics and finance problems, with examples based on: Prediction, Causal Inference and Policy Evaluation.
No additional information available.
This module will be delivered via:
The teaching will be designed to be inclusive to all, including those with additional learning needs. In the lab sessions, students will attempt to solve both theoretical and practical machine learning problems (which will be based in R).
-
James, G.
et al. (2022)
Introduction to Statistical Learning. 2nd ed. 2021. New York, NY: Springer-Verlag New York Inc. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=6686746.
-
Athey, S. and Imbens, G.W. (2019) ‘Machine Learning Methods That Economists Should Know About’,
Annual Review of Economics, 11(1), pp. 685–725. Available at:
https://doi.org/10.1146/annurev-economics-080217-053433.
-
Dube, Arindrajit (no date) ‘Monopsony in Online Labor Markets’,
American Economic Review: Insights, 2(1), pp. 33–46. Available at:
https://doi.org/DOI: 10.1257/aeri.20180150.
-
Cengiz, D.
et al. (2022) ‘Seeing beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes’,
Journal of Labor Economics, 40(S1), pp. S203–S247. Available at:
https://doi.org/10.1086/718497.
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 |
Assignment |
|
100% |
| Exam |
Main exam: In-Person, Open Book, 120 minutes during Summer (Main Period)
|
| Exam |
Reassessment Main exam: In-Person, Open Book, 120 minutes during September (Reassessment Period)
|
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 David Zentler-Munro, email: david.zentler-munro@essex.ac.uk.
Lectures and Classes: Dr David Zentler-Munro
For further information, send an email message to pgteco@essex.ac.uk.
Yes
Yes
Yes
Dr Domenico Moro
university of Birmingham
Lecturer
Available via Moodle
Of 32 hours, 28 (87.5%) hours available to students:
4 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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