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
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
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
See the library reading list for this module (Canterbury)
On successfully completing the module students will be able to:
1 Be able to understand the theoretical constraints facing computation, including the types of problems that cannot be solved by computers.
2 Be able to understand the impact of these constraints on the design of algorithms.
3 Be able to understand the effect of programming practice on the efficiency of the computation of an algorithm
4 Be familiar with the Matlab environment, including the statistics and optimisation toolboxes, as well as Dynare.
5 Be able to understand and run basic solution methods in Matlab for economic problems such as monte-carlo analysis, constrained optimisation, value function iteration and parallel computing.
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