The module will help students to build upon the work undertaken in Modelling and Estimation, by both broadening and deepening students' knowledge of econometric methods. They will also be able to develop a critical appreciation of the uses and shortcomings of various econometric methods and techniques whereby they will be introduced to certain problems involved in modelling and forecasting with time-series data. This module will also enhance students' statistical and analytical skills to the point where they are able to approach the analysis and interpretation of economic data with confidence and experience and they will also be able to explore a wide range of topical applications of econometrics.

There are numerous econometric problems in the data available for empirical testing, This module concentrates on the introduction of econometric tools to analyze empirically, ways of identifying and dealing with these problems whereby they will be exposed to diagnostic tests and criteria for choosing models. Selected applications such as: modelling both at micro and macro level such as aggregate consumption, inflation, National Income to name a few will be introduced. Topics include:
• Introduction to Time Series Analysis
• Econometric problems revisited: multicollinearity,
• Econometric problems revisited :Model mis-specification
• Econometric problems revisited: serial correlation
• Econometric problems revisited: heteroscedasticity.
• Diagnostic tests and criteria for choosing models
• Modelling philosophies: general-to-specific versus specific-to-general approaches.
• Testing for stability of coefficients.
• Selected applications such as: modelling aggregate consumption, modelling demand for selected foodstuffs, modelling inflation, the econometrics of advertising, the econometrics of demographic change, wage equations, housing models.
• Special problems of using time-series data.
• Introduction to selected forecasting techniques.
• Basic introduction to logit models

Action Learning. Learning is achieved by engaging students in activities that have elements of problem solving combined with intentional learning.

Authentic Learning. Students will be presented with activities that are framed around "real life" contexts in which students will find learning more meaningful and motivating. Thus they will be more engaged in the process of acquiring knowledge.

Assessment strategy will be both formative and summative. Formative assessment strategies are used to test the current level of understanding and progress and to provide feedback to teacher and learner and to guide the next phase of learning. Types of formative assessments for this course will be discussions on tutorial questions, short quizzes and assignments. Summative assessment is used both in the mid and at the end of the programme formally to assess a learner’s skill, knowledge and understanding gained in this course.

For this module formative assessment will be undertaken through regular completion of tutorial work along with quizzes to check knowledge and understanding of basic concepts within accounting. To prepare students for the end of module summative exam, a mid semester test will take place under exam conditions.

Final Examination (duration 2 hours). This will be a closed book exam testing all learning outcomes - 100%
Learning outcomes 1, 2, 3 & 4.

BUV VLE

Main Texts:
Studenmund, A.H. (2010) Using Econometrics: A Practical Guide, 6th ed. (Addison-Wesley Series in
Economics

Additional Texts:
Dougherty, C. Introduction to Econometrics, 3rd ed. (Oxford, 2007)
Thomas, R.L. Modern Econometrics: An Introduction (Addison-Wesley, 1997

1. CRITICALLY APPRAISE CONCEPTS AND ASSUMPTIONS UNDERLYING THE ECONOMETRIC AND TIME-SERIES METHODS CONSIDERED IN THE MODULE
Knowledge and Understanding, Learning

2. DEMONSTRATE AN UNDERSTANDING OF STANDARD ECONOMETRIC APPROACHES TO TESTING ECONOMIC THEORIES USING APPROPRIATE DATA
Knowledge and Understanding, Application

3. CRITICALLY EVALUATE VARIOUS ALTERNATIVE ECONOMETRIC AND TIME-SERIES METHODS
Analysis, Problem Solving

4. CRITICALLY EVALUATE THEIR OWN AND OTHER RESEARCHERS' STATISTICAL FINDINGS
Analysis, Reflection, Communication