This module aims to introduce students to econometric and statistical methods, which are essential for conducting advanced empirical research in corporate finance and accounting.

(LO1) Students will be able to design and implement econometric analysis for research questions.

(LO2) Students will be able to demonstrate a critical understanding of the pros and cons of each econometric method.

(LO3) Students will be able to collect data from various data sources and manage them efficiently.

(LO4) Students will be able to demonstrate an advanced understanding of the statistical characteristics of the data used in empirical analysis.

(LO5) Students will be able to apply a suitable econometric method that can control the statistical characteristics of the data used in empirical analysis.

(LO6) Students will be able to interpret the statistical implications of results by econometric analysis.

(LO7) Students will be able to critically evaluate research papers in the field of empirical corporate finance and accounting.

(S1) Organisation skills
This is relevant for planning scheduled work and meeting assessment deadlines. This will be evident in the students’ independent management of their assignments and coursework and by meeting coursework deadlines.

(S2) Problem solving and decision-making
Students will be challenged to think critically about organisational issues and dilemmas. They will do this by gathering and synthesising information, analysing alternative perspectives and options and presenting a considered opinion or course of action in their course assessment.

(S3) Digital skills
Students will develop IT skills through learning STATA programming and data management with Excel.

(S4) Numeracy
Students will develop numeracy through computation in the coursework assignment and the seminar tasks.

(S5) Communication skills
Students will develop written communication skills in the coursework assignment.

(S6) Ethical awareness
Students will enhance their ethical awareness by reflecting how research including their own is consistent with the principles of research ethics, integrity and open science and learning how to resolve issues and navigate challenges through in class work and completing coursework.

(S7) International awareness
International issues will be covered in related group and individual activities.

Lectures x 24 hours (2 hours per week)

Seminars x 12 hours (1 hour per week)

Self-directed learning x 164 hours
Self-directed learning hours will be spent on reading the module textbooks and journal articles, working through examples, and solving problems and exercises additional to those covered in learning materials, and reflecting on implications for own research.

Basic Regression Analysis:
Linear regression models
Limitations: Omitted variables, endogeneity, sample selection
Other issues

Panel Data Analysis:
Fixed effects (FE) and random effects (RE)
Robust standard errors

Instrumental Variables:
Selection principles
Validity of instrumental variables
Other methods to address endogeneity

Matching Methods & Natural Experiments:
Treatment vs. control
Difference-in-Difference estimator
Propensity-score matching and entropy balancing.
Triple difference etc.

Regression Discontinuity Design

Nonlinear & Dynamic Models:
Limited dependent variables: Probit/Logit
Censored dependent variables: Tobit
Lagged dependent variables: GMM

Event Studies:
Corporate events: M&As, dividend announcement
Short-term and long-term event studies

Advanced Topics: Textual Analysis

Advanced Topics: Network Analysis