APPLIED ECONOMETRICS - 2022/3
Module code: ECOM058
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 during 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.
This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting, with an emphasis on time series and panel data. The techniques are applied to real data with the use of an econometric package.
RISPOLI Luciano (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: L110
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 11
Independent Learning Hours: 95
Lecture Hours: 11
Guided Learning: 22
Captured Content: 11
Prerequisites / Co-requisites
Indicative content includes:
- Multiple Regression analysis
- Functional form
- Econometric models with time series
- Panel data methods
|Assessment type||Unit of assessment||Weighting|
|Examination Online||Final Examination||50|
The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques. This module has a technical and a practical component. Assessment emphasises work based on econometric and statistical packages in the form of an assignment, in which students are asked to analyse real economic and financial data. The technical component is assessed via a final examination. Students benefit from assessments as these are a chance for students to showcase their level of understanding about the subject both in terms of theoretical notions and capability to apply the theory to real world examples.
Thus, the summative assessment for this module consists of:
- 50% Coursework Assignment (linked to learning outcomes 1& 2) and
- 50% Final Examination: (linked to learning outcomes 1-4)
Formative assessment and feedback:
This is done by specific, individualised written comments, feedback meetings with students and general feedback in class.
- This module aims to provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation econometric models, with an emphasis on time series and panel data. 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. This module also equips students with the necessary analytical, statistical and software skills required to successfully undertake the writing of the dissertation within the module ECOM055 Advanced Economics Project.
|001||Systematically understand the principles of estimation and hypothesis testing in a multivariate CKT setting and be critically aware of the assumptions made in building econometric models||KCT||EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|002||Demonstrate comprehensive knowledge of the properties of different estimators and tests||KCT||EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|003||Demonstrate a practical understanding of the application of econometric techniques to actual data using computer packages||KCPT||DIGITAL CAPABILITIES; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|004||Proficiently use the time series and panel data testing and estimation capabilities of a range of software packages, evaluating the relative merits of competing methodologies||KCPT||DIGITAL CAPABILITIES; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
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:
- readings using the lecturer's guidance and solving exercises
- responding to questions in class and office hours
- a 2-hour lecture per week x 11 weeks aimed at exploring and discussing topics of theoretical nature
- a one-hour workshop per week x 11 weeks aimed at implementing the theory learnt in class to real word scenarios
- 22 hours guided learning aimed at deepening the understanding of topics and complementing with extra readings the material seen in the lectures
Students are expected to actively engage in workshops and to interact with the lecturer and colleagues. This is to promote an active rather than passive approach to learning.
These methods have been chosen so as to develop an appreciation of real word econometric application and their usefulness in finance and economics. Further, through these methods students will also appreciate the benefits deriving from applying scientific rigorous approaches to solve theoretical as well as applied problems. Furthermore, these methods clearly feedback the applicability of the key pillars.as outlined in the learning outcomes. For instance, through the application of rigorous techniques to real world problems students will be expected to develop skills linked to the Employability and Resourcefulness and Resilience pillars.
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: ECOM058
In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Resourcefulness and Resilience
Self-evaluation is built into assessment process, creating necessary space for students to reflect on own performance, whilst reviewing and asking for specific feedback.
Assessment mimics professional scenarios and real word situations experienced by applied economists/econometricians.
Students will acquire knowledge of important soft skills such as proficiency in the use of econometric software.
Programmes this module appears in
|Economics MA||2||Compulsory||A weighted aggregate mark of 50% 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.