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Economics Quantitatively Treated 1

  • Module code: EC4004
  • Year: 2018/9
  • Level: Year 4
  • Credits: 30.00
  • Pre-requisites: None
  • Co-requisites: None

Summary

This module provides an introduction to mathematical and statistical techniques; students are prompted to appreciate how mathematical reasoning is used in economics and develop skills in the numerical, graphical and statistical analysis of economic data.  The course starts with a review of material that may have been encountered in students' previous studies, such as mathematics at GCSE or IB level, and moves on to developing their knowledge, understanding  and ability to apply quantitative concepts, of particular relevance for microeconomics, macroeconomics and econometrics.

Aims

  • To provide an introduction to elements of mathematics applied in the study of economics and the application of numerical, graphical and statistical analysis.
  • Raise the level of awareness of students regarding the mathematical reasoning used in economics.
  • Develop an appreciation of the importance of statistics when conducting preliminary analysis in economics.
  • Equip students with the essential foundations for an understanding and ability to use the models developed in micro and macro economics and for further study in the quantitative treatment of economics.

Learning outcomes

On successful completion of the module, students will be able to:

  • Demonstrate competence in basic algebraic manipulation and knowledge of introductory maths techniques.
  • Graph linear equations and solve simple and simultaneous equations.
  • Express an economic model using mathematical symbols and undertake comparative static analysis.
  • Use the techniques required to measure the gradient of a curve and apply these in discovering the optimal point of a function.
  • Locate, identify and summarise data using graphical and numerical techniques.
  • Apply and interpret methods of estimation and testing based on the normal and students' t probability distributions.
  • Employ and interpret concepts regarding the probability of an event happening.
  • Apply elementary smoothing and decomposition methods to time-series data, construct and interpret price indices.

Curriculum content

  • Introductory maths and basic algebra: arithmetic operators, concept of numbers, algebraic manipulations, exponents, logs, sets.
  • Linear functions: graphs, calculating from co-ordinates, points of intersection, simultaneous equations. 
  • Applications of linear equations in economics: budget constraints, demand and supply analysis, taxes & subsidies, cost and revenue break even.
  • Non-linear equations: plotting curves, quadratic equations, roots.
  • Calculus: derivatives as slopes, power rule, turning points for optima, second order conditions, differentiating a range of functions, economic examples.
  • Sources of data and sampling methodologies.
  • Introduction to descriptive and inferential statistics.
  • Calculation and interpretation of measures of location and variation.  Raw and grouped data along with various graphical representations.
  • Introduction to the normal probability distribution. Inferential statistics; hypothesis testing and Confidence Intervals.  Student's t distribution.
  • Probability theory and statistical inference.  Expected values.  The application and analysis of; joint, marginal and conditional probabilities and the concepts of; statistical independence, covariance and correlation.
  • The application of elementary smoothing and decomposition methods to time-series data.
  • Construction and interpretation of index numbers.

Teaching and learning strategy

Students are divided into groups that meet weekly and receive three hours class-contact each week.  Each group is conceived of as receiving a combination of lecture delivery, tutorial discussion and exercise practice.  In practice however the boundaries between a lecture and tutorial or seminar can be fluid in the context of maths and statistics teaching.* The three hours are used to deliver and extend concepts, respond to student queries, provide hands on experience of working with/applying mathematical and statistical concepts and thereby build the confidence and capacity of students to work within the medium of quantitative methods. Preparatory reading is required for each session and comprehensive on-line material is available for each topic.  Regular worksheets are to be completed by students and discussed in class.  Students will also be encouraged to work through problems together.

Formative assessment and feedback is a critical part of the learning strategy.  Major elements of this include:

-  Formative class tests

-  Self & tutor assessment on progress in completing the regular worksheets

-  Self assessment on progress from completing a variety of designated on-line exercises and materials. 

-  Coursework feedback

Breakdown of Teaching and Learning Hours

Definitive KIS Category Indicative Description Hours
Scheduled learning and teaching 22 two hour classes with a mixture of lecture delivery, tutorial discussion and exercise material. 44
Scheduled learning and teaching 22 one hour classes with a mixture of tutorial discussion and exercise material. 22
Guided independent study Student independent study 234
Total (number of credits x 10) 300

Assessment strategy

Summative Assessment for this module consists of 2 elements as follows:

Coursework Portfolio: Three reports and/or posters, focusing on descriptive data analysis and inferential statistics.                                                                              

Exam: An invigilated examination of 2 hours duration during the summer examination period.   No outside material allowed, other than calculators.

The first form of summative assessment provides the opportunity for focus, feedback and a strengthening of the student's foundation in quantitative skills.

The second summative assessment is a two hour exam paper at the end of the module aimed at assessing student achievement of learning outcomes and preparedness for the next level

Mapping of Learning Outcomes to Assessment Strategy (Indicative)

Learning Outcome Assessment Strategy
Demonstrate competence in basic algebraic manipulation and knowledge of introductory maths techniques. Coursework/Exam
Graph linear equations and solve simple and simultaneous equations. Coursework/Exam
Express an economic model using mathematical symbols and undertake comparative static analysis. Coursework/Exam
Use the techniques required to measure the gradient of a curve and apply these in discovering the optimal point of a function. Coursework/Exam
Locate, identify and summarise data using graphical and numerical techniques. Coursework/Exam
Apply and interpret methods of estimation and testing based on the normal and Student's t probability distributions. Coursework/Exam
Employ and interpret concepts regarding the probability of an event happening. Coursework/Exam
Apply elementary smoothing and decomposition methods to time-series data, construct and interpret price indices. Coursework/Exam

Breakdown of Major Categories of Assessment

Assessment Type Assessment Name Assessment Weighting
CWK Portfolio of Three Reports & Posters 50
EXWR Examination 50
Total (to equal 100%) 100%

Achieving a pass

It IS NOT a requirement that any major assessment category is passed separately in order to achieve an overall pass for the module

Bibliography core texts

Renshaw, G (2011), Maths for Economics, 3rd ed, Oxford, Oxford University Press.

Barrow, M (2009), Statistics for Economics, Accounting and Business Studies, 5th ed. Harlow, Pearson.

Bibliography recommended reading

Maths material:

Jacques, I (2009), Mathematics for Economics and Business, 6th ed, Harlow, Pearson.

Bradley, T (2008), Essential Mathematics for Economics and Business. 3rd ed, Chichester, John Wiley & Sons

Croft, A and Davison R (2006), Foundation Maths, 4th ed, Harlow, Pearson.

Taylor, R and Hawkins, S (2008), Mathematics for Economics and Business, Maidenhead, McGraw Hill.

Statistics material:

Oakshott, L (2009), Essential Quantitative Methods for Business Management and Finance,  4th ed. Palgrave

Orris, J.B. (2007), Basic statistics using Excel and Megastat,  International Edition. McGraw-Hill.

Salvatore, D. (2002).  Theory and Problems of Statistics and Econometrics,  Schaum's Outlne Series.  McGraw –Hill.

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