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MSc in Econometrics and Mathematical Economics

Programme Code: TMEM

Department: Economics

For students starting this programme of study in 2021/22

Guidelines for interpreting programme regulations

Classification scheme for the award of a taught master's degree (four units)
Exam sub-board local rules

Academic-year programme. Students must take courses to the value of four full units as shown. Students are also required to attend the introductory course EC451 Introductory Course for MSc EME.

Please note that places are limited on some optional courses. Admission onto any particular course is not guaranteed and may be subject to timetabling constraints and/or students meeting specific prerequisite requirements.


Course number, title (unit value)

Introductory course

EC451 Introductory Course for MSc EME (0.0)

Paper 1

EC484 Econometric Analysis (1.0) #

Paper 2

EC487 Advanced Microeconomics (1.0) #

Paper 3

EC417 Advanced Macroeconomics (1.0) #

Paper 4

MSc EME Option List - courses to the value of 1.0 unit from the following:


EC421 International Economics (1.0) #


EC423 Labour Economics (1.0) #


EC424 Monetary Economics and Aggregate Fluctuations (1.0) #


EC426 Public Economics (1.0) #


EC427 The Economics of Industry (1.0) #


EC428 Development and Growth (1.0) #


EC453 Political Economy (1.0) #


EC465 Economic Growth, Development, and Capitalism in Historical Perspective (1.0) #


EC475 Quantitative Economics (1.0) #


EC476 Contracts and Organisations (1.0) #


EC485 Further Topics in Econometrics (1.0) #


FM421 Applied Corporate Finance (0.5) #


FM429 Asset Markets A (0.5) #


FM430 Corporate Finance and Asset Markets (1.0) #


FM431L Corporate Finance A (0.5) #


FM431M Corporate Finance A (0.5) #


FM441 Derivatives (0.5) #


FM442 Quantitative Methods for Finance and Risk Analysis (0.5) #


FM445 Portfolio Management (0.5)


MY459 Special Topics in Quantitative Analysis: Quantitative Text Analysis (0.5) #


MY461 Social Network Analysis (0.5)


MY474 Applied Machine Learning for Social Science (0.5) #


ST409 Stochastic Processes (0.5) #


ST418 Non-Linear Dynamics and the Analysis of Real Time Series (0.5) #  (not available 2021/22)


ST422 Time Series (0.5) #


ST443 Machine Learning and Data Mining (0.5) #


ST444 Computational Data Science (0.5) #


ST446 Distributed Computing for Big Data (0.5) #


ST449 Artificial Intelligence (0.5)


ST451 Bayesian Machine Learning (0.5) #

# means there may be prerequisites for this course. Please view the course guide for more information.

The Bologna Process facilitates comparability and compatibility between higher education systems across the European Higher Education Area. Some of the School's taught master's programmes are nine or ten months in duration. If you wish to proceed from these programmes to higher study in EHEA countries other than the UK, you should be aware that their recognition for such purposes is not guaranteed, due to the way in which ECTS credits are calculated.

Note for prospective students:
For changes to graduate course and programme information for the next academic session, please see the graduate summary page for prospective students. Changes to course and programme information for future academic sessions can be found on the graduate summary page for future students.