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Mathematical Research Methods - ECO00006D

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  • Department: Economics and Related Studies
  • Credit value: 20 credits
  • Credit level: D
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

This module introduces students to formulating and analyzing economic problems in a mathematically rigorous manner.

Module will run

Occurrence Teaching period
A Semester 1 2023-24

Module aims

The module is aimed primarily at those doctoral students who wish to work in areas in which they will have to read material that is technically demanding. There will be lectures on optimization, a narrow area that will be studied in some depth at an adequate level, with emphasis on both an intuitive grasp of the material (by using geometry in the exposition and examples) and on formal proofs. The topics covered include elementary analysis, Lagrange’s method, convex analysis, separation theorems, Kuhn-Tucker result on local maximization, concave programming, quasiconcave programming, Euler-Lagrange conditions, discrete time dynamic programming under certainty, and a study of the corresponding Bellman Equation. The lectures will be followed by student presentations on topics chosen with the lecturer’s approval.

Module learning outcomes

On completing the course of lectures the student is expected to recognize a proof, and to identify the technique appropriate for resolving optimization problems that one encounters in economics. By extension, the training should permit the student to access other tools from mathematics that are used in economic analysis. The presentations will allow students to develop the technical skills necessary to engage in research in areas of their choice.

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Additional assessment information

75% based on the student's presentation including answers to questions asked by the lecturer and fellow students, which might require a written submission, and 25% based on a short report on another student's presentation and responses to any follow-up questions by the lecturer on the report.

in the event that a reassessment is deemed necessary, the format will be as close as practically possible to the structure of the "first attempt

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Feedback will be provided in line with University policy

Indicative reading

  • SUNDARAM, R. K.: A First Course in Optimization Theory, Cambridge, 1996.

  • DIXIT, A. K.: Optimization in Economic Theory, Oxford, 1976/1990.

  • MANGASARIAN, O. L.: Nonlinear Programming, McGraw Hill, 1969.

  • RUDIN, W.: Principles of Mathematical Analysis, McGraw Hill, 1976.

  • STOKEY, N. L., AND R. E. LUCAS: Recursive Methods in Economic Dynamics, Harvard, 1989.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.