Module FINN41715: Econometric Methods

Department: Finance

FINN41715: Econometric Methods

Prerequisites

• None.

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• to provide students with the essential econometrics skills necessary to pursue empirical research in finance;
• to provide a basis for understanding more advanced econometric techniques to be taught in the second term of the MSc.

Content

• This module aims to introduce the students to the modern econometric techniques and to provide hands-on experience in applying those to solve different problems in economics and finance.
• The lectures will focus on the theory and intuition behind various techniques and in the workshops we will consider applications with real data and discuss the seminal academic papers that have introduced or employed those techniques.
• The topics to be covered include:
• linear regression model: ordinary least squares (OLS) estimator, OLS properties, statistical inference; violations of OLS assumptions: detection, consequences and solutions;
• simultaneous equations model (SEM) and endogeneity: indirect least squares (ILS), two-stage least squares (2SLS), instrumental variables (IV), Hausman test;
• panel data: fixed effects (FE) and random effects (RE) estimators, difference-in-difference (DiD) estimator;
• machine learning: classification and regression problems, ridge and lasso regressions, knn, decision trees, bias-variance trade-off, cross-validation.

Learning Outcomes

• advanced knowledge of both theoretical and practical aspects of the key econometric concepts, principles and methods.

• ability to apply econometric methods to data and interpret the results;
• ability to use the learnt econometric methods to conduct their own empirical investigations;
• handling large datasets and using them in conjunction with the appropriate techniques to solve various problems in economics and finance;
• using R to conduct econometric analysis, as well as to import and manipulate data.

• computer literacy and programming skills (through using R in workshops and for the project);
• interpersonal and written communication skills (through working in a team on the written project);
• problem solving and analytical skills (through applying learnt techniques to solve problems and interpreting estimation results);
• planning, organising and time management skills (through meeting the multiple deadlines for formative and summative assignments of the module).

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The module is delivered through a combination of lectures and workshops to facilitate the balance of theory and practice. The practical component will be taught using R (RStudio) during the workshops (with up to 80 students in a workshop group).
• The students are assessed through the summative and formative assignments. The summative assignment consists of: (1) a written group project and (2) an examination to test knowledge of key econometrics methods, concepts and principles. The formative assignment consists of an in-class quiz taking place during the workshop in week 6. The in-class quiz contains multiple-choice questions and true/false questions.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Mid-term in-class quiz.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University