This course provides an introduction to the theory and practice of econometrics, and presents a treatment of econometric principles for cross-sectional and time series data sets. The course concentrates on linear models and focuses on how the techniques can be applied in practice rather than on how their statistical properties can be rigorously derived. The essential purpose of the module is to meet the main empirical research needs of students who typically do not intend to specialise in econometric theory. However, the module also serves as a preparation for students who do wish to proceed to more advanced econometrics courses. Students are expected to have gained experience and show competence in the following transferable skills: data generation, IT (using statistical and econometric software), results interpretation and technical write-up, team-working, directed Web based searches, and use of library resources.

Introduction; regression analysis; method of Ordinary Least Squares (OLS); the Classical Linear Regression Model; properties of OLS estimators - Gauss-Markov theorem; interval estimation and hypothesis testing; multiple regression analysis; heteroscedasticity; autocorrelation; multicollinearity; dynamic econometric models - autoregressive and distributed-lag models; time series econometrics (including stationarity, unit roots and cointegration). The course makes use of Excel, Microfit 4.1 and Stata data analysis and statistical software.

On completion of this module, students should be able to: Explain how theory and method can be applied to solve practical problems. Describe the process used when conducting an empirical econometric investigation. Show an understanding of the principles of linear regression analysis. Present the verbal, graphical, mathematical and econometric representation of economic relationships. Select appropriate econometric techniques to quantify the economic relationship under investigation. Formulate suitable single-equation econometric models for the empirical study of economic phenomena. Demonstrate an ability to apply the various estimation techniques through the use of data analysis and statistical software. Interpret parameters and test hypotheses in single-equation econometric models. Organise project work from beginning to completion.

Students should be able to: Value the role and relevance of econometrics in applied economic research. Question the limitations of the applicability of econometrics in economic science.

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This module will be taught through a combination of lectures and practical computer laboratory classes, as well as private study hours. Students are expected to take responsibility for their own independent learning, and should be pro-active in their engagement with the learning materials. Additionally, this module aims to foster other key attributes such as being knowledgeable, collaborative and technically articulate. To promote self-learning and learning-by-doing, continuous assessment by means of two significant group project assignments (40% of the overall assessment) is built into the module. The learning experience includes the formulation and technical (mathematical and econometric) specification of research design, data sourcing, econometric software, results generation and interpretation as well as a demonstrated review of relevant analytical and empirical economics literature. A bespoke course-book (c. 90 pages) and a practitioner's laboratory manual (c.60 pages), written by the lecturer, is provided to students taking the course. Transferable skills include the use of word processing, Excel, econometric software and directed Website searches; oral and written communication; project work as part of a team; the use library and Web based resources; and time management to achieve goals.