Econometrics

Course Description

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.

Learning Outcomes

Upon Completion of the programme, students should be able:

• 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;
• 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;
• Illustrate the application of this theory within a range of economic and financial contexts, introducing the art of model building;
• Familiarize students with a standard statistical/econometric software package (e.g. Views).
• Develop students' skills, in particular: applied analysis; critical thinking; problem solving; academic study skills; self assessment and reflection; and quantitative analysis.

Course Content

• 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).

Compulsory Reading Materials

• Gujarati, D.N. and Porter D. 2010. Essentials of Econometrics; 4th edition. McGraw Hill.

Optional Reading Materials

• 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.