module specification

This module focuses on the theory and application of the Classical Linear Regression Model, the violation of its assumptions and its extensions.  The module provides students with the knowledge and skills to design, undertake, and evaluate empirical work within economics, finance and business.

EC4007 Quantitative Methods for Economics

This module aims to:

1. develop the basic concepts of regression analysis, providing a firm grounding in the theory of Ordinary Least Squares (OLS) and an appreciation of its limitations;
2. provide a theoretical understanding of the causes, consequences and detection of, and remedies for, the violation of the assumptions of the classical linear regression model;
3. illustrate the application of this theory within a range of economic and financial contexts, introducing the art of model building;
4. familiarise students with a standard statistical/econometric software package (e.g. EViews).

The module also aims to develop students' skills, in particular: applied analysis; critical thinking; problem solving; academic study skills; self assessment and reflection; and quantitative analysis.

Introduction to empirical methods (Econometrics): economic theory versus empirics.
Review of basic statistics: random variables, probability distributions and sampling theory.
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.
Development of IT quantitative software including development of intermediate knowledge of spreadsheets. Using workbooks. Organising and managing data including sorting and filters. Solving problems by analysing data. Solving what-if problems.
The Classical Linear Regression Model: specification, estimation, hypothesis-testing and
Functional form and non-linearity: dummy variables and transformation of variables.
Violations of the assumptions of the classical model: autocorrelation, heteroscedasticity, measurement error, multicollinearity and specification errors.
Dynamic models: distributed lag.
Simultaneous-equations models: basic issues, identification and estimation methods.
Introduction to discrete choice models:
Using and interpreting the output of dedicated econometric software (e.g. Eviews).

Learning consists of ‘formal’ class room learning directed by the teaching team, and reflective independent learning. The formal learning involves lectures, seminars and computer-workshops while the independent learning consists of reading of the course material, working on weekly exercises including  computing assignments using software (for example Eviews) and coursework that involves undertaking econometric analysis and writing a report, and preparing for the final written exam.

The module is delivered in a four-hour session each week which comprises a two-hour lecture, a one-hour seminar and a one-hour computer workshop. In the seminar students present their solution(s) to the problems set and raise questions on the lecture material. In the computer workshop students undertake empirical analysis using IT software. The seminar and the workshop provide opportunities for active and reflective learning, and also formative feedback. A virtual learning environment (Weblearn) will support blended learning by providing lecture notes and seminar material, previous assessment with feedback, and other material.

All activities provide students with the basic knowledge of econometrics and statistics. The weekly exercises and the coursework give students such diverse skills as working independently, problem solving, writing concisely and clearly, retrieving secondary data from various online sources and describing and exploring them using spreadsheets or econometric software.

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

1. describe and make use of basic econometric techniques both when the classical linear regression assumptions are satisfied and when they are not;
2. apply appropriate econometric techniques to problems of economics and finance and be able to develop parsimonious models using a systematic modelling strategy;
3. interpret, evaluate and explain regression results and test hypotheses;
4. use at least one piece of dedicated statistical/econometric software (e.g. Excel and EViews) .

The assessment strategy is developed with the aim of testing the module's learning outcomes. Students will be assessed by both formative and summative assessment through coursework and unseen examination.

The in-class test, which takes place towards the end of the first term, will test students’ understanding of key principles and concepts developed in the module. The test will give students helpful feedback on their strengths and weaknesses in this technical subject.

The applied econometric coursework will be based on a computing assignment that applies econometric methods to a particular economic model using dedicated statistical/econometric software. The exercise will be written up in a report. It will assess students’ knowledge and skills in designing, executing and evaluating empirical work within a range of economic and financial contexts.

The unseen and seen written examination will primarily provide a thorough assessment of students' theoretical knowledge and ability to interpret, evaluate and explain econometric results.

Gujarati, D.N. and Porter D. 2010. Essentials of Econometrics; 4th edition. McGraw Hill.
Gujarati, D.N. and Porter D. 2009. Basic Econometrics; 5th edition. McGraw Hill.
Gujarati, D (2011). Econometrics by Example. Palgrave MacMillan.
Asteriou, D and Hall S G (2011). Applied Econometrics, Palgrave
Macmillan (2nd edition).Dougherty, C. 2010. Introduction to Econometrics, 4th edition, Oxford.
Wooldridge, J. M. 2009. Introduction to Econometrics 4th ed., South Western College Publishing.