SOAS University of London

Department of Economics

Quantitative methods for Economists

Module Code:
Unit value:
Year of study:
Year 1 or Year 2
Taught in:
Full Year

This course is offered to students who have at least a B in A-Level maths (or equivalent) attained. The course offers a survey of applied mathematical analysis and economic statistical techniques useful for intermediate and advanced economic theory. The course is an essential prerequisite for Econometrics.

This is a core course for the BSc Economics and compulsory for BSc Development Economics and BA Economics and ... (two subjects degree with Economics to appear first in title). Introduction to Quantitative Methods for Economists is a prerequisite for the course if taken in year two. Students with A level mathematics or equivalent may be permitted to take this course in Year 1, subject to the year tutor's approval. It is a pre-requisite for Econometrics.



(153400120) Introduction to Quantitative Methods for Economists 

Objectives and learning outcomes of the module

On successful completion of the course, students should be able to:

  • apply matrix and vector operations
  • solve systems of equations using matrix algebra
  • use Jacobian determinants to test for functional dependence
  • demonstrate understanding of derivatives, rules of differentiation, partial derivatives, differentials and total differentials, higher order derivatives, their uses and applications
  • find unconstrained and constrained optima for multivariate functions and use these techniques to solve problems in economics
  • understand indefinite integrals and apply them to economic problems
  • understand differential equations and difference equations and apply them to economic problems
  • use index numbers to describe changes in economic measures
  • use Lorenz curve and Gini coefficient to describe inequality
  • explain and apply concepts in probability theory
  • use characteristics or moments of probability density functions
  • use population parameters and sample estimators
  • demonstrate understanding of and ability to use distribution, chi-square, t and F distributions
  • explain the sampling distribution of an estimator
  • demonstrate understanding of measures of goodness to fit including their uses and limitations
  • explain the assumptions of the classical linear regression model
  • use hypothesis test on regression coefficients

Method of assessment

Assessment weighting: Exam 80%, Assignments 20%

Suggested reading


  • Chian, A.C. and K. Wainwright (2005) Fundamental Methods of Mathematical Economics, Fourth Edition. McGraw-Hill.
  • Jacques, I. (2009) Mathematics for Economics and Business, 5th Edition, Prentice Hall. 
  • Wisniewski, M. (2003) Mathematics for Economics, 3rd Edition, Palgrave Macmillan.


  • Barrow, M. (2009) Statistics for Economics Accounting and Business Studies, 5th Edition, Prentice-Hall.
  • Diez, D., C. Barr and M. Cetinkaya-Rundel (2015) Openintro Statistics, 3rd Edition, Openintro.
  • Illowsky, B., et al. (2013) Introductory Statistics, Openstax College.
  • Larsen, R. J. and Marx, M. L. (2011) An Introduction to Mathematical Statistics and Its Applications, Pearson Education



Important notice regarding changes to programmes and modules