Aims and Fit of Module

The aim of this module is to give students an understanding of advanced econometric methodology. This module will build upon the materials of basic econometrics. Important micro-econometric methods, such as discrete choice models, and time-series methods, such as multivariate models will be covered.

Learning outcomes

A. Understand the basic components of maximum likelihood estimation.
B. Understand the theoretical foundation of micro-econometric models.
C. Apply appropriate micro-econometric model to data and interpret estimation results.
D. Specify and demonstrate the distributional characteristics of a range of time series models.
E. Estimate appropriate models of financial and economic time series for the purpose of forecasting and inference.
F. Apply univariate and multivariate model selection and evaluation methods.

Method of teaching and learning

The module will be delivered by a combination of lectures and tutorials. Lectures will be designed to provide essential information and introduce students to the basic tools and concepts of advanced econometric methods. Tutorials will provide students with the opportunity to further develop their skills through the exploration of various theoretical and practical problems, illustrated via actual data sets and real world problems from economics and finance.