QUANTITATIVE METHODS - 2017/8

Module code: MANM280

Module provider

Surrey Business School

Module Leader

PAL S Prof (SBS)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 7

JACs code

G300

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Workshop Hours: 10

Independent Study Hours: 117

Lecture Hours: 22

Seminar Hours: 10

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test MID-TERM TEST (50 MINUTES) 30%
Examination 120 MINUTES EXAMINATION 70%

Alternative Assessment

Not applicable

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.

Module overview

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.

Module aims

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.

Learning outcomes

Attributes Developed
Understand the principles of estimation and hypothesis testing KC
Know the properties of ordinary least square estimators KC
To be able to apply econometric techniques to actual data KC
To be able to critically evaluate hypotheses using data KC
Using E-views/Stata software to estimate, predict linear/non-linear regression models and also perform various diagnostic tests. T
Use the technical and software skills acquired for evaluating various practical assignments including the compulsory masters dissertation P

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

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

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

Assessment Strategy


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

Reading list

Reading list for QUANTITATIVE METHODS : http://aspire.surrey.ac.uk/modules/manm280

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 2017/8 academic year.