GY428 Half Unit
Applied Quantitative Methods
This information is for the 2023/24 session.
Dr Stephen Jarvis
Prof. Hendrik Wolff
This course is compulsory on the MSc in Environmental Economics and Climate Change and MSc in Environmental Policy, Technology and Health (Environmental Economics and Climate Change) (LSE and Peking University). This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Environmental Economics, MPhil/PhD in Regional and Urban Planning Studies, MSc in Geographic Data Science, MSc in Local Economic Development and MSc in Urban Policy (LSE and Sciences Po). This course is available with permission as an outside option to students on other programmes where regulations permit.
The number of students that can be accommodated is limited. If the course is over-subscribed, places will be allocated at the Department’s discretion and a waiting list may be created. For further details, please contact your relevant Programme Coordinator.
A background in undergraduate statistics or, preferably, econometrics is required
This course will provide an introduction to quantitative methods in use in modern environmental and resource economics. Emphasis will be placed on the practical use of empirical tools. This applied focus will be complemented by the investigation of assumptions and proofs that can improve the understanding of empirical results. Students will apply the methods taught using statistical/econometric software and data documenting some topical public policy questions. These applications will take place in ten seminars of one hour each. During the seminars the students will gain understanding of the statistical programming language R. Throughout the course, examples from relevant and topical empirical papers published in the area of applied econometrics and environmental economics will be critically discussed. The module will focus on linear regression methods, with an emphasis on their use for causal inference. The first part of the course will cover the standard linear regression model, its assumptions, violations and testing procedures. Functional forms and non-linear models will also be discussed. The latter part of the course will cover a range of important estimation approaches, including fixed effects with panel data, difference-in-differences, instrumental variables and regression discontinuity designs. The course will conclude with a more general discussion of how these tools can be used in research and policy analysis.
In the Department of Geography and Environment, teaching will be delivered through a combination of classes/seminars, pre-recorded lectures, live online lectures and other supplementary interactive live activities.
This course is delivered through a combination of classes and lectures across Autumn Term. This course includes a reading week in Week 6 of Autumn Term.
There will be an opportunity to get feedback on one or more of the problem sets assigned during the AT.
Detailed reading lists will be provided to support each course component, but the following texts will be particularly useful:
- Stock J.H. and M.W. Watson (2019). Introduction to Econometrics. Fourth Edition Pearson International Edition;
- J. Wooldridge (2006), Introductory Econometrics: A Modern Approach, Thomson;
- Angrist J and Pischke J.S. (2014) Mastering ‘Metrics, Princeton.
- Angrist J and Pischke J.S. (2009) Mostly Harmless Econometrics, Princeton.
- Cunningham S. (2021) Causal Inference The Mixtape, Yale.
Exam (70%, duration: 2 hours) in the January exam period.
Coursework (30%) in the AT.
The coursework assessement will take the form of problem sets or exercises that recap on some of the most important topics.
Student performance results
(2019/20 - 2021/22 combined)
|% of students
Department: Geography and Environment
Total students 2022/23: 62
Average class size 2022/23: 16
Controlled access 2022/23: Yes
Lecture capture used 2022/23: Yes (MT)
Value: Half Unit
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Personal development skills
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills