This course covers a range of methods used in the modern application of econometric techniques to economic and financial data. Following a review of linear regression methods, students are introduced to maximum likelihood estimation and instrumental variable methods, before proceeding to a more in-depth treatment of certain classes of models that arise in economics and finance to deal with particular types of data. These include: limited dependent variables; univariate and multivariate time series (stationary and nonstationary); and panel data. Issues of model specification, estimation and hypothesis testing will be stressed throughout. The course is designed to enable students to practise the relevant methods, rather than to derive estimators or tests, or to prove the theorems upon which these are based.
Upon completion of the course, students will be able to demonstrate their knowledge of modern econometric methods and be able to analyse economic data using the appropriate techniques. In completing the course test, students will demonstrate their problem-solving analytical and deductive skills.
Employability skills include: Academic skills: Literacy, numeracy, problem-solving and ICT skills; Professional working skills: adaptability; flexibility, decision-making; External awareness: economic and business environment and policy; Personal development planning: Time management, self management, reflection and evaluation.
Upon completion of the course, students will be able to demonstrate their knowledge of modern econometric methods and be able to analyse economic data using the appropriate techniques. In completing the course test, students will demonstrate their problem-solving analytical and deductive skills.
Employability skills include: Academic skills: Literacy, numeracy, problem-solving and ICT skills; Professional working skills: adaptability; flexibility, decision-making; External awareness: economic and business environment and policy; Personal development planning: Time management, self management, reflection and evaluation.
- Module Supervisor: Neslihan Sakarya