This unit provides an introduction to basic statistics and its applications in economics and business disciplines. Topics include: methods for data management; analysis and interpretation of data; probability; the normal distribution; an introduction to sampling theory and hypothesis testing; and the concepts of regression analysis. A key component is the provision of instruction and experience in the use of computers and statistical software as an aid in the analysis of data. Students are expected to use data resources on the world wide web, retrieve data and analyse this data using Excel.

This focus of this unit is to develop a student's understanding of regression analysis. The unit covers how linear regression models can be used to estimate relationships, to forecast, and to test hypotheses that arise in economics and business. Guidelines for using econometric techniques effectively are discussed and students are introduced to the process of model building. To develop a student's understanding of regression, economic applications are emphasised. This unit also makes extensive use of econometric software.

Students undertaking this unit have some background in basic statistics including an introduction to regression analysis. Using this knowledge as a base, an extensive discussion of basic regression theory and some of its extensions is provided. The unit covers how linear regression models can be applied to data to estimate relationships, to forecast, and to test hypotheses that arise in economics and business. Guidelines for using econometric techniques effectively are discussed and students are introduced to the process of model building. It is essential that the discussion of regression modelling be complemented with practice in analysing data. An important task is the computing component using econometric software.

Over the last decade econometric modelling of financial data has become an important part of the operations of merchant banks and major trading houses and a vibrant area of employment for econometricians. This unit provides an introduction to some of the widely used econometric models for financial data and the procedures used to estimate them. Special emphasis is placed upon empirical work and applied analysis of real market data. Topics covered may include the statistical characteristics of financial data, the specification, estimation and testing of asset pricing models, the analysis of high frequency financial data, and the modelling of volatility in financial returns.

This unit extends methods of estimation and testing developed in association with regression analysis to cover econometric models involving special aspects of behaviour and of data. In particular, motivating examples are drawn from dynamic models, panel data and simultaneous equation models. In order to provide the statistical tools to be able to compare alternative methods of estimation and testing, both small sample and asymptotic properties are developed and discussed.

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Applied Econometrics is concerned with the appropriate use of these techniques in practical applications in economics and business. General principles for undertaking applied work are discussed and necessary research skills developed. In particular, the links between econometric models and the underlying substantive knowledge or theory for the application are stressed. Topics will include error correction models, unit roots and cointegration and models for cross section data, including limited dependent variables. Research papers involving empirical research are studied and the unit features all students participating in a group project involving econometric modelling.

The need to forecast or predict future values of economic time series arises frequently in many branches of applied economic and commercial work. It is, moreover, a topic which lends itself naturally to econometric and statistical treatment. The specific feature which distinguishes time series from other data is that the order in which the sample is recorded is of relevance. As a result of this, a substantial body of statistical methodology has developed. This unit provides an introduction to methods of time series analysis and forecasting. The material covered is primarily time domain methods designed for a single series and includes the building of linear time series models, the theory and practice of univariate forecasting and the use of regression methods for forecasting. Throughout the unit a balance between theory and practical application is maintained.

This unit studies and develops the econometric models and methods employed for the analysis of data arising in financial markets. It extends and complements the material covered in ECMT2130. The unit will cover econometric models that have proven useful for the analysis of both synchronous and non-synchronous financial time series data over the last two decades. Modern Statistical methodology will be introduced for the estimation of such models. The econometric models and associated methods of estimation will be applied to the analysis of a number of financial datasets. Students will be encouraged to undertake hands-on analysis using an appropriate computing package. Topics covered include: Discrete time financial time series models for asset returns; modelling and forecasting conditional volatility; Value at Risk and modern market risk measurement and management; modelling of high frequency and/or non-synchronous financial data and the econometrics of market microstructure issues. The focus of the unit will be in the econometric models and methods that have been developed recently in the area of financial econometrics and their application to modelling and forecasting market risk measures.

The Honours program in Econometrics consists of: (1) a thesis not exceeding 65 A4 pages of typescript written under the supervision of one, or more, members of academic staff, and (2) four semester-length coursework options that each meet once a week for three hours. Students take two coursework options in their first semester, and two in their second semester. Assessment details vary per coursework option, however all contain a final examination worth up to 50% with the remaining balance comprised of written work and oral presentations, and (3) a (non assessed) weekly meeting with the thesis supervisor in both semesters.

See ECMT4101

See ECMT4101

See ECMT4101