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Econometric Analysis

Module description

Summary:

This module covers more advanced modelling and statistical inference techniques for single and multi-equation systems and the use of these techniques for prediction and model evaluation.

Additional Information:

Internationalisation

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.

Employability

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.

Sustainability

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

Module aims - intentions of the module

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. 

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 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

On successfully completing the module you will be able to...

• 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

On successfully completing the module you will be able to...

• 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.

Syllabus plan

• Revision of Matrix Algebra and Statistical Theory
• The Linear Regression Model
• Statistical Inference
• Asymptotic Theory
• Instrumental Variables
• Linear Regression with Time-Series Data
• Linear Regression with Panel Data

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

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

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

Indicative learning resources - Basic reading

Basic Reading:

The course will not follow any single text, and you are recommended to read widely. Useful texts include:

• Cameron, C. and Trivedi, P. K. (2010) Microeconometrics using Stata, Stata Press
• Davidson, J. (2018) Econometric Theory, Wiley
• Greene, W. (2012) Econometric Analysis, Pearson Education/Prentice Hall
• Hayashi, F. (2000) Econometrics, Princeton University Press
• Wooldridge, J. M. (2020) Introductory Econometrics: A Modern Approach, Cengage Learning
• Wooldridge, J. M. (2002) Econometric Analysis of Cross Section and Panel Data, MIT Press