QUANTITATIVE METHODS - 2022/3
Module code: MANM280
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.
This module lays the statistical and econometric foundations for subsequent applied work, covering fundamental topics of estimation and inference of linear and non-linear econometric models using E-views software. The quantitative, analytical and software skills acquired from this module will directly enable these students to conduct independent quantitative analysis for estimation/testing various hypotheses as part of their Masters dissertations. As such, the module aims to help students to develop an understanding of the research method and to undertake research leading to successful completion of their dissertation.
Surrey Business School
PAL Sarmistha (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G300
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 10
Independent Learning Hours: 105
Lecture Hours: 13
Seminar Hours: 11
Captured Content: 11
Prerequisites / Co-requisites
Basic knowledge of secondary level Mathematics including linear equations, natural logarithms, laws of exponents and simple differentiation is assumed in constructing this module. We will run preliminary Mathematics/Statistics primer course in the beginning of the term for students to revise/review the essential background materials.
The following is an indication of the likely topics to be covered:
- Population, sample and data description.
- OLS regression and its properties
- Bivariate and multivariate regression models
- Functional forms and estimation of non-linear models
- Dummy explanatory variables
- Diagnostic tests: Multicollinearity and Heteroskedasticity
|Assessment type||Unit of assessment||Weighting|
|CLASS TEST SET DATE AND TIME (50 MIN)||30|
|EXAM SET DATE AND TIME (120 MIN)||70|
The assessment strategy is designed to provide students with the opportunity to demonstrate their knowledge of theoretical and empirical issues of the subject
Thus, the summative assessment for this module consists of:
Mid-term test (30%): This will be a 50 minute test based on materials covered in lectures during weeks 1-5 – this will test their understanding of some key concepts
Examination (70%) In the examination students will need to answer two out of four questions covering both theoretical and empirical issues taught in lectures. This will test their ability to explain key theoretical concepts and analyse empirical results.
Formative assessment and feedback
Students will receive verbal feedback from the seminar discussions
Students will receive correct answers and exam feedback for mid-term test paper
Students will go through last year's exam paper for exam preparation
- To enable students to handle cross-section and time-series data and also to use various statistical techniques to describe data, produce and analyse correlations and scatter diagrams
- To provide an introduction to linear and non-linear model building and then train them to estimate various bivariate and multivariate models using Eviews/Stata
- To enable students to test hypotheses, generate predicted values and examine diagnostic statistics.
- By covering the fundamentals of research methods and research methodologies, this module will enable students to conduct research independently and provide them with the knowledge and understanding needed to do a dissertation.
|1||Understand the principles of estimation and hypothesis testing||KC|
|2||Know the properties of ordinary least square estimators||KC|
|3||To be able to apply econometric techniques to actual data||KC|
|4||To be able to critically evaluate hypotheses using data||KC|
|5||Using E-views/Stata software to estimate, predict linear/non-linear regression models and also perform various diagnostic tests.||T|
|6||Use the technical and software skills acquired for evaluating various practical assignments including the compulsory masters dissertation||P|
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 include the following:
Maths/Stats Primer lectures held in week 1 of the term
Lectures (22 hrs) using Powerpoint slides available online from week 1 onwards
Seminars (5 hrs for each group, every alternative week, starting in week 2)
Computer workshops (once a week, starting in week 1)
Seminar preparation will include
Reading lecturer slides and textbook
Preparing answers to seminar worksheet using E-views
Responding to questions in seminars and receiving feedback from the lecturer
Preparing for the mid-term test and the assignment (please see assessment strategy below)
Computer workshop (once a week)
How to use E-views/Stata to solve seminar questions using different data-sets
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: MANM280
Programmes this module appears in
|International Financial Management MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|International Corporate Finance MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Accounting and Finance MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics MA||1||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.