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EC475     
Quantitative Economics

This information is for the 2024/25 session.

Teacher responsible

Dr Michael Gmeiner (Autumn Term) SAL 4.28

Dr Xavier Jaravel (Winter Term) SAL 3.15

Availability

This course is available on the MSc in Econometrics and Mathematical Economics and MSc in Economics. This course is available with permission as an outside option to students on other programmes where regulations permit.

Pre-requisites

Students must have completed Introductory Course for MSc EME (EC451) or Introductory Course in Mathematics and Statistics (EC400).

Knowledge of econometric theory and applied econometrics is expected. Students must be prepared to read journal articles with difficult mathematical and statistical content.

Course content

The course will focus on going through modern quantitative papers which demonstrate the application of econometric techniques to modelling the behaviour of individual economic agents (households and firms) and economies.

The first half of the course focuses on papers in the empirical literature on a wide range of topics in applied micro-econometrics including industrial organisation, labour economics, economic history, and energy/environmental economics. Papers are chosen to illustrate the challenges of identification and causal inference. The focus of the content is on methods, however the papers covered as examples of those methods use data and study economic questions from a variety of countries and settings. A goal is to provide a diverse view of economic research. The lectures will illustrate the interplay between models, data, and methods.

The second part of the course focuses on macroeconomic questions using data and tools from applied microeconomics. We cover four styles of empirical work: (1) “reduced-form” approaches (including difference-in-differences, event studies, instrumental variables, and Bartik research designs); (2) structural models; (3) “sufficient statistics” research designs, at the intersection of structural and reduced-form methods; and (4) machine learning techniques. Topics covered include the effectiveness of fiscal stimulus, the measurement of inflation, directed technical change, the welfare effects of trade, the macroeconomic impact of financial frictions over the business cycle, the macroeconomic impact of unemployment insurance, and the effect of Artificial Intelligence on the labour market.

Teaching

20 hours of lectures and 9 hours of seminars in the AT. 20 hours of lectures and 10 hours of seminars in the WT. 1 hour of seminars in the ST.

There will be a reading week in Week 6 of AT and in Week 6 of WT (no lectures or classes in those weeks).

This course is delivered through a combination of classes and lectures totalling a minimum of 60 hours across Autumn Term, Winter Term and Spring Term.

Formative coursework

During Autumn term and Winter term, students will work on their essay and receive feedback from the instructors (defining the research question, choosing a research design, etc.). In Autumn term formative assignments will move students toward creating an idea for their paper and referee reports on papers that are discussed in seminars. Formative assignments in Winter term will be presentations of research papers and providing feedback to peers.

Indicative reading

Articles in economic journals will be assigned at the start of Autumn and Winter terms. The course will also draw on methodological topics covered in Wooldridge, Econometric Analysis of Cross Section and Panel Data (2nd edition, 2010), Greene, Econometric Analysis (7th edition, 2012), and Angrist and Pischke, Mostly Harmless Econometrics (2009).

Assessment

Exam (50%, duration: 2 hours, reading time: 15 minutes) in the spring exam period.
Essay (50%, 6000 words) in the ST.

Key facts

Department: Economics

Total students 2023/24: 15

Average class size 2023/24: 16

Controlled access 2023/24: Yes

Value: One Unit

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.