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This subject examines multiple regression analysis and its use in economics, management, finance, accounting and marketing. Topics will include the properties of estimators, hypothesis testing, specification error, multicollinearity, dummy variables, heteroskedasticity, serial correlation. Empirical assignments undertaken by the student form an integral part of the subject.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Apply the classical model of ordinary least squares to data sets drawn from economics, finance, accounting and management using single and multiple regression equations;
- Test hypotheses concerning the relationship between variables;
- Explain in detail the consequences of the violation of any one of the classical assumptions;
- Test for violations of the classical assumptions;
- Estimate models in the presence of non-classical errors and stochastic explanatory variables;
- Diagnose model misspecification using the most appropriate tests, and where appropriate identify the appropriate remedial actions;
- Use computer software to perform simple data descriptions and to graph relationships between variables, to estimate econometric models using OLS and Instrumental Variables, and to estimate simple dynamic models;
- Apply econometric methods to real world data and perform diagnostic testing to ensure the model is adequately specified.
High level of development: written communication; application of theory to practice; interpretation and analysis; critical thinking; synthesis of data and other information; evaluation of data and other information; use of computer software.
Moderate level of development: problem solving; statistical reasoning; accessing data and other information from a range of sources.
Some level of development: oral communication; collaborative learning; receptiveness to alternative ideas.
Last updated: 3 December 2022