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.

A proper understanding of econometric methods and estimation techniques is important, as it allows researchers and practitioners to assess economic theorems, predict macroeconomic tendencies, evaluate government policies, etc. After a brief review of probability and statistics, this unit will focus on the econometric analysis of discrete variables, and the econometric analysis of time series data. The lectures and assessments will be application-oriented. Computer software (e.g., Stata, R, Matlab) 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 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 unit is designed for third year students to undertake a project that allows them to work with one of the University's industry and community partners. Students will work in teams on a real-world problem provided by the partner. This experience will allow students to apply their academic skills and disciplinary knowledge to a real-world issue in an authentic and meaningful way.