This unit emphasises understanding the use of computing technology for data description and statistical inference. Both classical and modern statistical techniques such as bootstrapping will be introduced. Students will develop an appreciation for both the usefulness and limitations of modern and classical theories in statistical inference. Computer software (e.g., Excel, StatKey) will be used for analysing real datasets.

This unit is intended to be an introduction to the classical linear regression model (CLRM), the underlying assumptions, and the problem of estimation. Further, we consider hypothesis testing, and interval estimation, and regressions with dummy variables and limited dependent variable models. Finally, we consider different functional forms of the regression model and the problem of heteroskedasticity. Throughout we will try to emphasise the essential interplay between econometric theory and economic applications.

This unit will provide an introduction to the key issues involved in with the econometrics of cross-section and panel data. The topics this unit will cover include: instrumental variables; estimating systems by OLS and GLS; simultaneous equation models; discrete-choice models; treatment effects; and sample selection. Throughout the unit, emphasis will be placed on economic applications of the models. The unit will utilise practical computer applications, where appropriate.

This unit focuses on time series techniques and more advanced econometrics methods (e.g. MLE, GMM, model specification analysis). This unit starts with a review of probability and statistics and cross sectional methods, followed by advanced methodologies that are useful for analysing time series data. The unit is ended with a selected list of special topics. The lectures and assessments will be application-oriented. Computer software (e.g., Stata, SAS, R) will be used throughout the unit.

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.

This unit provides an accessible foundation in the principles of probability and mathematical statistics that underlie the statistical techniques employed in the fields of econometrics and management science. These principles are applied to various modelling situations and decision making problems in business and economics.

This unit provides an introduction to modern computationally intensive algorithms, their implementation and application for carrying out statistical inference on econometric models. Students will learn modern programming techniques such as Monte Carlo simulation and parallel computing to solve econometric problems. The computational methods of inference include Bayesian approach, bootstrapping and other iterative algorithms for estimation of parameters in complex econometric models. Meanwhile, students will be able to acquire at least one statistical programming language.

This unit of study is designed to provide students with various topics in applied microeconomics. Estimation of the labour supply elasticity, returns to schooling, and returns to training programs are examples of topics this unit will cover. Various empirical topics in international trade, environmental economics, and health economics will also be discussed. Students will explore econometric methodologies extensively used in applied microeconomics (e.g., instrument variables, generalise methods of moments, panel data methods, probit and logit models, Tobit model, and sample selection model).

This unit provides an introduction to econometric theory and methods that can be useful for understanding applied (mostly macroeconomic/finance) models and research. It also aims to provide students with the necessary analytical tools for undertaking applied research using time series data and discusses how time series techniques can be applied to other areas of economics such as international trade, energy economics, economics of terrorism. This unit can be both complementary to and substitutive for Applied Microeconometrics, which focuses on empirical methods in applied microeconometrics.

This unit of study surveys contemporary research in labour economics. The field of labour economics is very broad, dealing with fundamental issues ranging from resource allocation to distributional equity and social welfare. The subject matter covers the determinants of wages, employment and unemployment; insurance and incentive mechanisms; and the behavioural effects and welfare impacts of institutions and public policies. In this unit students will have the opportunity to analyse theoretical models and their empirical applications.

This unit of study is designed to train students in current theoretical and empirical developments in the field of development economics. Specific topics change from time to time as development economics can cover most fields of economics with a particular application to developing countries. Examples of topics include: development finance; firms in emerging markets; poverty traps and social interactions; and history and institutions in the context of economic development.

This unit concentrates on mainstream models and estimation and inference methods that are widely used in most empirical investigations in applied microeconomics. The unit has a topics-based structure, and theory and applications are closely integrated. Examples of topics include parametric and semi-parametric estimation methods applied to cross-section and panel data; treatment evaluation; models of cross-sectional dependence; quantile and mixture regressions; density estimation; Bayesian regression analysis.

The unit is designed to provide an understanding of selected topics of current academic research in the area of advanced empirical macroeconomics. The course develops tools and reviews basic models of business cycles and monetary policy. The unit then applies these tools and models to actual macroeconomic data to enhance understanding of the workings of these models, with an emphasis on their merits and shortcomings.

Research in economics, finance, marketing and accounting has been enriched by increased availability of panel data. A 'panel' refers to the pooling of observations on a cross section of households, countries, firms or individuals over several time periods, offering major advantages over conventional cross-sectional or time series data sets. This unit teaches students a comprehensive set of tools for the analysis of panel data, enabling students to both critically assess and contribute to applied economic research.

Study of an advanced topic in Econometrics. Topic may vary from semester to semester according to staff availability and the presence of visitors. Examples of topics include: Bayesian Econometrics; Nonparametric and Semiparametric Econometrics; Econometrics for Big Data; Spatial Econometrics; and Financial Econometrics. This unit of study will develop advanced econometric techniques to equip students to undertake postgraduate studies in economics.

Study of an advanced topic in Econometrics. Topic may vary from semester to semester according to staff availability and the presence of visitors. Examples of topics include: Bayesian Econometrics; Nonparametric and Semiparametric Econometrics; Econometrics for Big Data; Spatial Econometrics; and Financial Econometrics. This unit of study will develop advanced econometric techniques to equip students to undertake postgraduate studies in economics.

In this unit you begin a substantial, independent research project in Econometrics. Regular meetings with a supervisor approved by the Economics Honours Coordinator will guide your progress. You will develop a plan for researching and writing the thesis, submit an ethics application if appropriate, familiarize yourself with disciplinary conventions and standards, engage with relevant literature, theories and methodologies, and submit drafts at agreed times..

In this unit students will complete a research project appropriate for a 15,000 word Econometrics Honours thesis. Each student will match with a research supervisor from the Economics who will give them feedback on their independent research.