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 basic economic statistical techniques and applied mathematical analysis 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 subject degree with Economics to appear first in title). Introduction to Quantitative Methods for Economists is a prerequisite for this 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:

  • use matrices for simple manipulations
  • solve system of equations using matrix algebra
  • use Jacobien determinants to test for functional dependence
  • demonstrate understanding of, derivatives, rules of differentiation, partial derivatives, total differential, higher order derivatives, their uses and applications
  • find unconstrained and constrained optima and use these techiques to solve problems in economics
  • understand indefinite and definite integrals and apply them to economic problems
  • use index numbers to describe changes in prices and quantity
  • use Lorentz curve and Gini coefficient to describe inequality
  • explain basic concepts in probability theory including, but not restricted to, experiment, population or sample space, sample point, event, mutually exclusive, equally likely and collectively exhaustive events, random variable, discrete and continuous random variable, probability, probability distribution or probability density function (PDF), statistical independence.
  • use characteristics or moments of PDF including, but not restricted to expected value, variance and standard deviation, skewness and kurtosis, covariance, coefficient of correlation, conditional and unconditional expectation
  • use population parameters and sample estimators including, but not restricted to sample mean, sample variance, sample standard deviation, sample covariance, sample correlation, sample skewness and sample kurtosis
  • demonstrate understanding of and ability to use normal distribution, chi-square, t and F distributions
  • explain the sampling distribution of an estimator (e.g. the sample mean)
  • 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%, weekly mini-tests 20%

Suggested reading

Background Reading


  • Abadir, K. M. and J. R. Magnus (2005) Matrix Algebra, Cambridge.
  • Chiang, A.C. and K. Wainwright (2005) Fundamental Methods of Mathematical Economics, Forth Edition. McGraw-Hill.
  • Jacques, I. (2009) Mathematics for Economics and Business, 5th Edition, Prentice Hall.
  • Wisniewski, M. (2013) 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.
  • Grinstead, C. and J. L. Snell (1997) Introduction to Probability, American Mathematical Society.  
  • 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