Computational Methods with Matlab - ECON8860

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Autumn Term 7 15 (7.5) Sylvain Barde checkmark-circle


The aim of the module is to train students to carry out numerical analysis of economic problems using the Matlab software package. The module is intended to be taken up by PhD students at an early stage of their degree with the aim of providing them with core knowledge of the software package (Matlab) and main methodologies that they will use during their degree and into their career. It is also intended to provide a solid foundation for subsequent PhD modules. Because of this, the module will combine both a theoretical component providing an introduction to the core concepts of computation and a practical component, using practical cases and examples carried out in terminal sessions.
Specific topics to be covered include:
• A brief history of computation.
• Introduction to the Matlab package.
• Good programming practice with Matlab
• Using toolboxes
• Parallel/distributed computing


Contact hours

Total contact hours: 18
Private study hours: 132
Total study hours: 150


Optional module on PhD Economics and PhD Agri-environmental Economics

Method of assessment

• Weekly Problems Sets (5 at 20% each)

Reassessment Instrument: 100% coursework

Indicative reading

• Attaway, S. (2012) Matlab: a practical introduction to programming and problem solving, 2nd ed., Elsevier.
• Brandimarte, P. (2006) Numerical methods in finance and economics: a MATLAB-based introduction
• Judd, K. (1998) Numerical Methods in Economics, MIT Press.
• Scott, J.C. (2009) But How Do It Know? - The Basic Principles of Computers for Everyone.

See the library reading list for this module (Canterbury)

Learning outcomes

On successfully completing the module students will be able to:

8.1. be able to understand the theoretical constraints facing computation, including the types of problems that cannot be solved by computers.

8.2. be able to understand the impact of these constraints on the design of algorithms.

8.3. be able to understand the effect of programming practice on the efficiency of the computation of an algorithm

8.4. be familiar with the Matlab environment, including the statistics and optimisation toolboxes, as well as Dynare.

8.5. Be able to understand and run basic solution methods in Matlab for economic problems such as monte-carlo analysis, constrained optimisation and value function iteration.

8.6. be able to run parallel or distributed jobs on Matlab.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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