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Quantitative Techniques for MSc

BUEM027S6 (30 credit)


Campus map Unless stated otherwise the classes will be in the Malet Street building

Lecturers: Sandeep Kapur, Ali Tasiran, Agnieszka Kulacka & Jose Camarena

September 2019

This compulsory course, taught in September, reviews the quantitative techniques required for our MSc programmes. The course comprises two modules:

  • September Mathematics: This module is for Full-Time students and for Part-Time students in their first year.
  • September Statistics: This module is taken by Full-time students, and also by Part-Time students just before the start of their second year.

Who must do this course?

Students on any of the following programmes are required to this course

  • MSc Economics
  • MSc Finance with advanced pathways
  • MSc Financial Economics
  • MSc Mathematical Finance
  • MSc Financial Risk Management


So who does what and when?

  • Full-time students must do both Mathematics (afternoon lectures) and Statistics (evening lectures).
  • Part-time students do only Mathematics (evening lectures) in this September and Statistics (evening lectures) next September

Lectures and problem-solving classes run on Mondays, Tuesdays and Thursdays for four weeks in September.

Resources for this course

Course material is on Moodle, our virtual learning platform. To access this, you need to have completed your enrolment formalities to the point where you are assigned an ‘ITS username and password’: these serve as your login credentials at

Once you are logged in, you should be able to see the courses you are enrolled for. This course should show up as ‘Quantitative Techniques (2019_20)’. If you can log in but don’t see this course, let the course lecturer know: they may be able to add you manually.

Moodle gives you access to many resources:

  • General guidance for this course including any updates
  • All the lecture material, including any errata
  • Access to practice Quizzes, and some online feedback
  • A Gradebook in which you can look up the results of your tests once they are released
  • Online videos (an experimental offering; not available for all sessions)
  • A chance for you to provide your anonymous evaluation for the lectures.


This module is worth 30 credits of the required 180 for the MSc programme. To obtain the MSc degree you MUST obtain a pass mark (50% or better) in this module. Assessment is based on multiple in-class tests. The dates for September 2019

Mathematics Test 1           12 September (Thursday, 2.15 pm and 6.15pm)

Statistics Test 1                 12 September (Thursday, 6.15pm)

Statistics Test 2                 25 September (Wednesday, 6.15pm)

Mathematics Test 2           26 September (Thursday, 6.15pm)

Unfortunately we are unable to make special arrangements for those who miss any of the tests.

For MSc programmes overall performance will depend on total of scores across the four tests. For Part-time students marks for the mathematics test done in September of their initial year is added to the marks for statistics tests done in their second year. You have to obtain 50% or better on average (underperformance in one test can be made up with better performance in another).

What happens if you don't get 50%? We review the performance of those whose raw score is below 50%, carrying out a clerical check and re-grading the scripts. Passing this course is also a requirement for progression, so if your mark on the completed module is less than 50%, you must contact your programme director. This is best done in the first week of term.

Feedback on your performance will come through various channels. You are provided summary feedback on your performance in each test soon after the test, via the Moodle grade book. At the end of all tests you may be able to get further guidance on your performance through a face-to-face meeting with the lecturers, but please hold this till early October.

Contacting the lecturers

The best way to initiate contact with the lecturers is via email. Depending on which module you are doing, here are the email address

Mathematics module 1 (weeks 1 & 2)        Sandeep Kapur

Mathematics module 2 (weeks 3 & 4)        Agnieszka Kulacka

Statistics module 1 (weeks 1 & 2)               Ali Tasiran       

Statistics module 2 (weeks 3 & 4)               Jose Camarena

September Mathematics

This module aims to help you

  • understand the basics of sets and functions;
  • understand the basics of linear algebra and the use of matrices;
  • learn how to find the constrained optima of multivariate functions;
  • compute definite and indefinite integrals;
  • solve simple difference and differential equations;
  • use these techniques to solve simple problems

Lecture notes are provided but these are not a substitute for a textbook. We do not recommend any particular text, but in the past students have found the following useful.

  • Chiang, Alpha and Kevin Wainwright, Fundamental Methods of Mathematical Economics, McGraw Hill, 4th ed, 2005.
  • Hoy, M., J. Livernois, C. McKenna, R. Rees and T Stengos, Mathematics for Economics, 2nd edition, MIT Press, 2001.
  • Simon, K. and L. Blume, Mathematics for Economics, WW Norton, 1994


September Statistics

This module covers the following topics

  • Probability and distribution theories (probability, random variables and probability distributions, expectations and moments, uni-variate and multi-variate distributions, functions of random variables)
  • Statistical Inference (sampling, large sample theory, point estimation, parametric interval estimation, tests of statistical hypotheses)

Lecture notes are provided but are not a substitute for a textbook. We do not recommend any particular text, but in the past students have found the following useful.

  • Wackerley D, Mendenhall W & R Scheaffer, Mathematical Statistics with Applications 7th ed, Cengage, 2008.
  • Greene, W H, Econometric Analysis 7th ed, Pearson, 2011.
  • Verbeek, M, A Guide to Modern Economics 4th ed, 2012



While course material is updated every year, we provide early access to past material to allow you to study early.

Mathematics [for Full-time and Part-time 1 students]
Mathematics materials for weeks 1 - 2 (2019/20)
Mathematics materials for week 3 (2019/20)
Mathematics materials for week 4 (2019/20)

Statistics [for Full-time and Part-time 2 students]
Weeks 1-2: Probability and Distribution Theories (2019/20)
Weeks 3-4: Statistical Inference (2019/20)