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 provides an introduction to the econometrics of cross-section and panel data. We start with a discussion of the assumptions underlying the simple and multiple linear regression model. We then build an understanding of the econometric methods available when these assumptions do not hold. More specifically, we cover heteroscedasticity and GLS, omitted variable bias, measurement error and instrumental variables. We finish with an introduction to using pooled cross sections and panel data for policy analysis and to estimate treatment effects. Throughout the unit, emphasis is placed on economic applications of the models and practical computer applications are incorporated.

This unit provides a thorough introduction to the econometrics of cross-section and panel data. We begin with a detailed discussion of the assumptions and statistical properties of the multiple linear regression model. We explore the econometric methods available when these assumptions do not hold. More specifically, we cover linear probability models, heteroscedasticity and GLS, omitted variable bias, measurement error, instrumental variables, quantile regression and models for pooled cross-section and panel data well-suited to policy analysis and the estimation of treatment effects. Throughout, we discuss economic applications and utilise practical computer applications where appropriate.

This unit develops foundations for more advanced study in econometrics. It begins with an introduction to matrix algebra, which is an essential part of the language of econometrics. This is followed by a formal introduction to probability and statistics, leading up to the law of large numbers and central limit theorem. Finally, these foundations are applied to the study of the linear regression model, the workhorse of econometric analysis.

This unit provides a detailed treatment of the core of modern econometric analysis, with a focus on understanding when and why econometric procedures can be expected to work. Topics covered include linear and nonlinear regression, generalised least squares, instrumental variables, generalised method of moments, and maximum likelihood. Numerical software will be used to illustrate the econometric procedures and concepts discussed.

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Econometric methods are concerned with the appropriate use of these techniques in practical applications in economics. In this unit 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 may include, panel data, partial identification, weak instruments, nonparametric regression and density estimation, and discrete choice models. Research papers involving empirical research are studied.

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 econometric methodologies. Its objective is to develop a deeper understanding of econometric methodologies encountered in intermediate units of study. The unit starts with the notion of a simple random experiment and extends it to the notion of datagenerating process for observational data in econometrics. The core topics of this unit include distribution finding techniques, identification in parametric and semiparametric models, estimation theory, and hypothesis testing, Monte Carlo techniques and the bootstrap.

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.

The unit introduces the theory and application of statistical machine learning. Topics covered include supervised versus unsupervised learning; regression and classification; resampling methods including cross-validation and Bootstrap; regularisation and shrinkage approaches such as Lasso; tree-based methods including decision tree and random forest; and support vector machines. The unit focuses on the applications of statistical machine learning in economics, and computer software such as R and Matlab are used throughout the unit.

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.

Students will apply microeconomic and econometric principles to problems in an interdisciplinary context. This unit builds on theoretical knowledge in microeconomics and applying new methods of micro-econometric analysis to real-world problems. Students will explore methodologies extensively used in applied microeconomics including instrument variables, GMM, panel data methods, probit and logit models, Tobit model, and sample selection models. Various empirical topics will be discussed in an interdisciplinary context. Students will have an opportunity to define a research problem, conduct a literature review, analyse data, and present research results in an interdisciplinary context.

This unit of study is concerned with the application of economic principles to problems in an interdisciplinary context. It builds on theoretical knowledge acquired in previous studies and introduces methods of applied economic analysis to real-world problems. Initially, a research problem will be presented by a guest lecturer. Supporting lectures will be delivered by the unit coordinator on the nature of research, appropriate theoretical concepts, quantitative methods and communication. Students will have an opportunity to define a research problem, conduct a literature review, analyse data, and present research results in an interdisciplinary context.

This interdisciplinary unit provides students with the opportunity to address complex problems identified by industry, community, and government organisations, and gain valuable experience in working across disciplinary boundaries. In collaboration with a major industry partner and an academic lead, students integrate their academic skills and knowledge by working in teams with students from a range of disciplinary backgrounds. This experience allows students to research, analyse and present solutions to a real¿world problem, and to build on their interpersonal and transferable skills by engaging with and learning from industry experts and presenting their ideas and solutions to the industry partner.