TIME SERIES ECONOMETRICS - 2022/3
Module code: ECO3003
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
By the end of the module students will have learnt how to carry out empirical analyses using appropriate econometric software to study economic and financial time series data; how to interpret the results of such analyses; and will have acquired an ability to critically assess empirical papers.
VOLPICELLA Alessio (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 74
Lecture Hours: 22
Tutorial Hours: 10
Guided Learning: 11
Captured Content: 33
Prerequisites / Co-requisites
Indicative content includes:
- The classical linear regression model: review of underlying statistical theory. Properties of estimators and test statistics.
- Stationary Time Series Models.
- Modeling Volatility in Financial Time Series.
- Dynamic models: distributed lags and models of expectations, error correction models.
- Econometric modelling methodology: general to specific modelling strategy for econometric time series models.
- VAR models.
- Random walks, tests for unit roots, structural breaks. Cointegration.
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of econometric methods that are commonly used in analysing time series data, and the ability to use relevant computer packages to investigate real world economic problems.
Thus, the summative assessment for this module consists of:
midterm to be worth 30% of the final mark;
final exam is worth 70% of the final mark.
Formative assessment and feedback
Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the midterm feedback is provided.
- Provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation time series econometric models. The module will equip the student with the ability to undertake, understand, and critically assess empirical work in economics, with a view to enabling the student to use econometrics to catalogue and describe empirical regularities and test various propositions.
|001||Understand the underlying statistical foundations of time series econometrics.||KCT|
|002||Critically assess published econometric results.||KCP|
|003||Formulate, estimate and interpret an econometric time series model.||KCPT|
|004||Write up the results of a study of an economic problem that includes econometric analysis.||KCPT|
|005||Proficiently use the time series testing and estimation capabilities of EViews package.||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Give students the theoretical tools they need to go out and analyse real world situations.
- Encourage rigour in their approach to problems.
- Encourage hands-on study of empirical problems.
The learning and teaching methods include:
- 2 hour lecture per week x 11 weeks.
- 1 hour tutorial/lab session per week x 10 weeks.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: ECO3003
Programmes this module appears in
|Business Economics BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics and Finance BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Politics and Economics BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics and Mathematics BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.