Econometric Analysis

Module description


Covers the more advanced modeling techniques for single and multi-equation systems, time series analysis, and the use of these techniques in forecasting and model evaluation.

Additional Information:


This module presents a mixture of econometric theory and applications with an emphasis on understanding the theory, but it is broadly a mathematical module. Since mathematics is an international language, the course content is relevant across the globe in theory and in practice.

Students are given tough material to master which helps them acquire advanced mathematical and numeracy skills which are highly valued by the majority of employers.

All the resources for the module are available on the ELE (Exeter Learning Environment).

Full module specification

Module title:Econometric Analysis
Module code:BEE3015
Module level:3
Academic year:2017/8
Module lecturers:
  • Professor James Davidson - Convenor
  • Dr Sebastian Kripfganz - Convenor
Module credit:30
ECTS value:



BEE2031 (BEE2006) and BEE2020



Duration of module: Duration (weeks) - term 1:


Duration (weeks) - term 2:


Module aims

This module aims to provide a thorough grounding in the modern theory of econometrics, together with working knowledge of the most important topics in econometric estimation and inference. Econometrics is now so large a subject that it is impossible to cover all areas of interest in one course. Coverage of econometric methods has to be selective. However, one essential objective is to achieve a sound, critical understanding of the theory of econometric inference – in other words, to know what claims can and cannot be made legitimately about the properties of estimates, tests and forecasts. This is more important in the long run than mastering a litany of formulae and techniques. Students will have the opportunity to apply the methods learned to real-world problems on the computer and learn to use econometric software packages. 

ILO: Module-specific skills

  • 1. demonstrate a comprehensive knowledge of the theory of econometric inference
  • 2. apply their understanding of econometrics to undertake empirical economic analysis and show the potential to contribute to knowledge, through original research

ILO: Discipline-specific skills

  • 3. demonstrate a clear understanding of the mathematical and statistical background of applied economics
  • 4. demonstrate the ability to critically assess, and carry out, empirical studies in economics
  • 5. use a computer for estimation and simulation exercise

ILO: Personal and key skills

  • 6. communicate effectively, using appropriate technical terms, in a variety of forms
  • 7. demonstrate a confident and flexible approach to identifying and defining complex problems and apply appropriate knowledge and methods to solve them
  • 8. analyse data and situations without guidance, using a range of appropriate techniques

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Contact hours40

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Tutorial questions 1 hour/ 2 weeks1-4, 6-8In class discussion

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Homework assignments, in the form of algebraic problems and data analyses301 homework in each semester1,2,3,4,5,6,7,8Class discussion
Examination703 hours1,3,4,6,7Written feedback as requested

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Homework Homework 1-8 To be decided when original work returned
Examination Examination 1, 3, 4, 6, 7August Examination period

Syllabus plan

Semester 1 - Sebastian Kripfganz

• Revision of Matrix Algebra and Statistical Theory
• The Linear Regression Model
• Specification Testing
• Asymptotic Theory
• Panel data 




Semester 2 - James Davidson

• Maximum Likelihood Estimation
• Linear regression in time series data
• Simultaneous equations analysis
• Univariate time series modelling
• Vector autoregressions
• Unit roots and cointegration



Indicative learning resources - Basic reading

Basic reading:
The course will not follow any single text, and you are recommended to read widely. The recommended one for purchase and thorough study this year is
Verbeek, M. (2004) A Guide to Modern Econometrics (2nd Edition) West Sussex: Wiley

Other texts which may be recommended during the module are:
Davidson, J. (2000) Econometric Theory, Oxford: Blackwell
Davidson, R. and MacKinnon, J. (2004) Econometric Theory and Methods, Oxford: OUP
Greene, W. (2012) Econometric Analysis, 7th Edition, Pearson Education/Prentice Hall
Johnston, J. and DiNardo, J. (1997) Econometric Methods, New York: McGraw Hill
Judge, G., Griffiths, W., Carter-Hill, R., Lutkepohl, H. and Lee, T. (1998) The Theory and Practice of Econometrics, 2nd Edition, West Sussex: Wiley

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