This course provides an introduction to applied econometrics for students with a background in economic statistics. Topics covered include economic models and the role of econometrics, linear and nonlinear regression, panel data regression, binary regression, IV regression, experiments and quasi-experiments, time series regression, and prediction with many regressors and big data. The practicality and limitations of specific econometric and statistical techniques are illustrated through examples and exercises drawn from various disciplines. Students gain hands-on experience applying these techniques using R.
ECON1310 is an introductory course in quantitative analysis for business and economics, covering techniques for presenting, interpreting, and utilising data. Topics include descriptive statistics, probability concepts, theoretical distributions, inferential statistics, and simple linear regression. Proficiency in Excel is required for analysis and presentation.
This course provides a working understanding of some of the principal techniques used in business and economics decision-making, known as operations research (OR) or management science (MS) techniques. Topics include linear and integer programming, data envelopment analysis, transportation and assignment models, project scheduling and control, inventory models, queueing theory, and decision theory and games. These techniques can be used to solve problems in areas as diverse as product mixing and blending, firm efficiency and benchmarking, project management, and multi-period financial planning. Problems and exercises are solved using the Excel QM software package.
This course introduces students to relevant mathematical methods and their application in economics. It covers topics in algebra, differential and integral calculus, unconstrained and constrained multivariate optimisation, and matrix algebra.
This course provides advanced finance and economics students with an applied understanding of econometric tools relevant to financial and macroeconomic data. The curriculum covers specific financial and macroeconomic models of stochastic processes and techniques for prediction, inference, and analysis of dynamic relationships. Core topics include stochastic processes; models of macroeconomic and financial processes; trends, cycles, and cointegration; conditional heteroscedasticity and volatility models; and multiple equation models. Applications are drawn from stock prices, derivatives, exchange rates, interest rates, high-frequency data, and market microstructure. Labs emphasise practical applications of these techniques using the R software package.
This course provides a comprehensive coverage of modern methods for analysing the productivity and efficiency of different types of decision-making units (e.g., individuals, firms, industries, regions, and economies). Students learn how different assumptions concerning technologies, markets and firm behaviour can be used to guide the construction of proper productivity indexes. They then learn how these indexes can be exhaustively decomposed into measures of technical change, environmental change, and various types of efficiency change. Students learn how to estimate these components using data envelopment analysis (DEA), deterministic frontier analysis (DFA) and stochastic frontier analysis (SFA) methods. Students gain an understanding of why the estimation of these components is critically important for public policy-making. The course has a strong applied focus. Tutorials involve analysing different datasets using R.
This course introduces students to relevant mathematical methods and their application in economics. It covers topics in algebra, differential and integral calculus, unconstrained and constrained multivariate optimisation, and matrix algebra.