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Economics Quantitatively Treated 2

  • Module code: EC5002
  • Year: 2018/9
  • Level: Year 5
  • Credits: 30.00
  • Pre-requisites: EC4004
  • Co-requisites: None

Summary

This module will extend knowledge of mathematical and statistical techniques acquired at level 4 and will introduce the student  to multivariate techniques in mathematics and statistics.

It will assist your comprehension of level 5 economics modules and encourage you to understand the benefits of using a mathematical and statistical vocabulary and reasoning to analyse economic models.

This module will equip you with sufficient quantitative techniques to be able to undertake any level 6 module in economics requiring quantitative analysis.

Aims

  • To assist student comprehension of their level 5 core micro and macro modules.
  • To equip students with sufficient quantitative techniques to be able to undertake any level 6 module in economics requiring some quantitative analysis.
  • To encourage students to understand the benefits of using a mathematical vocabulary and reasoning to analyse economic models.

Learning outcomes

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

  • Work through assigned mathematical and statistical exercises relevant to the syllabus content.
  • Design and carry out mathematical analysis of economic questions, employing the techniques of multi-variable calculus and matrix algebra.
  • Understand the role and scope of econometrics in constructing and testing models in economics.
  • Appreciate the limitations of these techniques and apply a variety of statistical tests to the models developed.
  • Use software for estimating and testing econometric models.

Curriculum content

  • Multivariate calculus – partial derivatives, total differential, total derivative.
  • Optimisation – multivariate functions – first and second order conditions.
  • Constrained Optimisation – applications to consumer and producer theory
  • Exponential and log functions – mathematics of finance
  • Introduction to basic rules of matrix algebra. Solving systems of linear equations.
  • Integration
  • Statistical Inference and properties of estimators
  • Basic linear regression model – estimation and testing.
  • Multiple Regression model
  • Regression diagnostics: multicollinearity, heteroscedasticity and autocorrelation.
  • Model specification errors

Teaching and learning strategy

The module will be taught in a workshop format.  Students will be divided into groups of 40 max students and each group will have three hours contact consisting of a two hour lecture and a one hour seminar. Students are expected to read the appropriate parts of the recommended texts and complete problem sets for the seminars. Some seminars will be held in computer rooms, where students will get to use a software package to construct and test econometric modules using different datasets.

Breakdown of Teaching and Learning Hours

Definitive KIS Category Indicative Description Hours
Scheduled learning and teaching Workshop - weekly 2 hour lecture 44
Scheduled learning and teaching Workshop - weekly 1 hour seminar 22
Guided independent study Preparation of weekly problem sets, Preparation for class tests and date handling assignment 132, 54
Guided independent study Revision of module material in preparation for final exam. 48
Total (number of credits x 10) 300

Assessment strategy

Formative assessment includes the weekly problem sets which cover mathematical and statistical theory and also applications to economics.

Summative assessment includes an unseen class test which will be based on the problem sets to be held in the last teaching week of Teaching Block 1, a group assignment based on an empirical econometrics exercise to be submitted in TW8 of Teaching Block 2 and a final unseen exam covering all of the module material.

Mapping of Learning Outcomes to Assessment Strategy (Indicative)

Learning Outcome Assessment Strategy
Work through assigned mathematical and statistical exercises relevant to the syllabus content. Problem sets
Design and carry out mathematical analysis of economic questions, employing the techniques of multi-variable calculus and matrix algebra. Problem sets, class test, exam.
Understand the role and scope of econometrics in constructing and testing models in economics. Problem sets, class test, group assignment, exam
Appreciate the limitations of these techniques and apply a variety of statistical tests to the models developed. Problem sets, group assignment, exam
Use software for estimating and testing econometric models. Group data handling assignment

Breakdown of Major Categories of Assessment

Assessment Type Assessment Name Assessment Weighting
PRC Unseen class test 25
CWK Group data handling assignment 25
EXWR Unseen 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

Required texts:

Renshaw, G (2011) Maths for Economics, Second Edition, Oxford

Gujarati, D. N. (2011) Econometrics by Example, Palgrave Macmillan

Bibliography recommended reading

Dowling , Edward T.(2011), Introduction to Mathematical Economics, 3rd Edtion , Schaum Outline Series

 Gujarati, D. N. & Porter, D (2009) Essentials of Econometrics, McGraw-Hill

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