# QUANTITATIVE METHODS - 2021/2

Module code: MANM280

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 provider

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

Module Availability

Semester 1

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 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

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

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:

1. 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

2. 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

1. Students will receive verbal feedback from the seminar discussions

2. Students will receive correct answers and exam feedback for mid-term test paper

3. Students will go through last year's exam paper for exam preparation

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
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Workshop Hours: 10

Independent Study Hours: 113

Lecture Hours: 22

Seminar Hours: 5

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.