# module specification

## EC4007 - Quantitative Methods in Economics (2024/25)

Module specification Module approved to run in 2024/25
Module title Quantitative Methods in Economics
Module level Certificate (04)
Credit rating for module 30
School Guildhall School of Business and Law
Total study hours 300

 81 hours Scheduled learning & teaching activities 219 hours Guided independent study
Assessment components
Type Weighting Qualifying mark Description
In-Course Test 30%   Short in class tests
Coursework 30%   Coursework (1000 words)
Unseen Examination 40%   unseen exam (2 hours)
Running in 2024/25

(Please note that module timeslots are subject to change)
No instances running in the year

## Module summary

This module is concerned with providing students with quantitative skills and a foundation in mathematical and statistical concepts and techniques so they can solve economic problems and understand economic analysis. Students will understand how mathematical and statistical methods relate to economic theory.

## Module aims

The main aims of the module are to:

1. provide an introduction to mathematical techniques required in economics;
2. introduce descriptive and inferential statistics;
3. apply mathematical and statistical techniques to economic problems;
4. provide opportunity for students to collect and analyse real data;
5. use appropriate spread sheet software to apply concepts learned.

## Syllabus

A. Mathematical Foundations
Arithmetic foundations
Introduction to algebra
Linear equations and applications to economics
Non-linear functions and applications to economic problems
Logarithmic and exponential function and their applications
Optimisation with one variable and applications to economics: Identifying maximum, minimum, and inflection points. Applications such as marginal cost, marginal revenue and elasticity. Maximising profit, total revenue and minimising average cost
Mathematics of finance and growth: compounding, discounting, net present values and internal rates of return
Optimisation with more than one variable and economic applications: Non- constrained and constrained optimisation
Integration and application to economics: Consumer and producer surplus
Matrix Algebra: vectors, matrices, matrix operations, solution of simultaneous equations
Index numbers: price, quantity and expenditure index numbers of single and multiple commodities
Using IT software to present mathematical concepts and relations.

B. Statistical Foundations
Data issues: collection, selection and sources. Use IT to access sources of relevant economic and financial information, and transform into usable information relevant to the analysis of business economics and finance.
Graphical data presentation using different techniques such as frequency distributions, pie and bar charts, and histograms
Summarising data using measures of average, dispersion and skew
Probability theory
Discrete probability distribution: Binomial probability distribution
Continuous probability distribution: Normal probability distribution
Sampling probability distribution
Point and interval estimation, small and large samples
Hypothesis testing, significance levels, errors
Using IT software to present statistical concepts and relations.

## Learning and teaching

Teaching is centred around three hours of weekly contact in lectures, seminars and workshops. The lecture workshop will involve presentation of the material in an interactive way allowing periods of reflection by students. Students will go through problem sheets provided in the module book and on Weblearn in seminars. Seminars will be used to develop IT skills and support weaker students. A virtual learning environment (Weblearn) will support learning and teaching activities in the module, containing lecture slides, seminar questions, past test papers, data sets and other learning materials.

## Learning outcomes

On successful completion of this module students will be able to:

1. understand some of the main mathematical tools employed in the subject areas of introductory economics, business and finance;
2. understand some of the main statistical tools employed in subject areas of introductory economics, business and finance
3. understand, collect, interpret, evaluate, manipulate, synthesise, represent and analyse quantitative data in a subject context;
4. apply and critically assess a range of different models and methodologies employed in undertaking quantitative analysis in the areas of economics, business and finance;
5. appropriately interpret and report results from an empirical exercise;
6. use IT quantitative software including spreadsheets to apply mathematical and statistical concepts.

## Bibliography

Bradley, T.  (2008) Essential Mathematics for Economics and Business, 3rd ed., John Wiley & Sons Ltd.
Bradley, T (2007) Essential Statistics for Economics, Business and Management, John Wiley & Sons Ltd.
Chiang, A. C. and Wainwright, K. (2005) Fundamental Methods of Mathematical Economics, 4th ed., McGraw-Hill.
Jacques, I. (2009) Mathematics for Economics and Business, 6th ed., FT Prentice Hall.
Oakshott, Les (2009) Essential Quantitative Methods for Business, Management and Finance, 4th ed., Palgrave Macmillan.
Renshaw, G. (2009) Mathematics for Economics, 2nd ed., Oxford University Press
Swift, L. (2010) Quantitative Methods for Business, Management and Finance, 3rd ed., Palgrave-Macmillan.
Sydsaeter, K. and Hammond, P. (2008) Essential Mathematics for Economic Analysis, 3rd ed., FT Prentice Hall.