The module introduces you to some important topics in advanced econometrics. The primary aim is to provide a deep and sound knowledge of modern econometric concepts and techniques. The first part of the module will give you an introduction to the probabilistic foundations, and discuss the advanced theory of estimation and inference. Subsequently, various special topics will be covered.
While this module is designed for those interested in theory and research, mathematics is an international language, making the course content relevant across the globe in theory and in practice.
This module is highly useful for those interested in conducting research in the field of econometrics. Also, students will have the opportunity to develop their numeracy skills, which are highly valued by many employers.
All of the resources for this module are available on the ELE (Exeter Learning Environment).
Full module specification
|Module title:||Advanced Econometrics|
|Duration of module:||
Duration (weeks) - term 2: |
The module is intended to introduce students to some important topics in advanced econometrics. The primary aim is to provide a deep and sound knowledge of modern econometric concepts and techniques. The first part of the module will give an introduction to the probabilistic foundations, and discuss the advanced theory of estimation and inference. Subsequently, various special topics will be covered.
ILO: Module-specific skills
- 1. a mastery of the underlying concepts in probability and their application in econometrics and the analysis of economic data.
- 2. knowledge of recent literature in this area.
ILO: Discipline-specific skills
- 3. Applied mathematical and statistical techniques
- 4. Computer programming skills.
ILO: Personal and key skills
- 5. Self-management/time-management
- 6. Locating / using learning resources (www, library)
- 7. Communication, oral and written
- 8. Problem solving
- 9. Data analysis
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Weekly problem sets||11 weeks||1-9||Answers on ELE discussion with module convenor if requested|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Examination||100||2.5 hours||1-9||Answers on ELE discussion with module convenor if requested|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Examination||Examination||1-9||August Examination period|
Weeks 1-2: Probabilistic fundamentals and stochastic process theory.
Weeks 3-4: Asymptotic theory, with applications to regression analysis.
Weeks 5-6: Advanced estimation theory – optimization estimators and maximum likelihood.
Week 7: Asymptotic theory for unit root and cointegration models.
Weeks 8-10: Special topics, including:
o Long memory processes; theory and applications.
o Nonlinear time series models, ARCH and Markov switching.
o Bootstrap methods for statistical inference.
Indicative learning resources - Basic reading
James Davidson, Econometric Theory, Blackwell Publishers 2000.
James Davidson, Stochastic Limit Theory, OUP 1994.
James Hamilton, Time Series Analysis. Princeton UP 1994
Russell Davidson and James MacKinnon,Econometric Theory and Methods. OUP 2002
Module has an active ELE page?
Last revision date