25934 Applied Econometrics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2025 is available in the Archives.

Subject level:

Undergraduate

Description

This subject extends student knowledge in financial econometrics to enable them to assess advanced research literature in finance. It teaches students practical applications of econometrical modelling techniques. Students gain expertise in developing, conducting, and evaluating econometric analyses for practical investment and portfolio decisions.

Subject learning objectives (SLOs)

Contribution to the development of graduate attributes

The subject provides students solid training in applied econometric analysis of financial assets and markets. It contributes particularly to the development of the following graduate attributes:

• Critical thinking, creativity and analytical skills
• Business practice oriented skills

This subject also contributes specifically to develop the following Program Learning Objectives:

• Apply critical thinking and analytical skills in the process of completing a research project (1.1)
• Apply the appropriate research method and analytical tools in addressing discipline specific problems (4.1)

Teaching and learning strategies

Students will have pre-readings to complete before in class lectures/seminars. Lectures are followed by discussions, student presentations, and hands-on computer exercises.

Content (topics)

1. Basic and robust OLS
2. Time series models
3. Panel data
4. Event studies
5. Simulation methods
6. Big data and machine learning

Assessment

Assessment task 1: In-class tests (Individual)*

Assessment task 2: Assignments (Individual)

Assessment task 3: Research Proposal (Individual)

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Required texts

[BROOKS] C. Brooks, 2014, Introductory Econometrics for Finance, 3rd edition, Cambridge University Press.

References

Below is a list of recommended texts if you would like to learn more about finance and economic application using particular econometric technique. Each textbook is abbreviated in square brackets and linked to subject schedule.

Introductory texts (OLS, hypothesis testing, time-series models and panel data models):

1. [G] Gujarati, D., Econometrics by Example, 1st or 2nd editions, Palgrave-Macmillan. Written primarily for undergraduate students in economics, accounting, finance, marketing, and related disciplines. It is also intended for students in MBA programs and for researchers in business, government, and research organizations. Econometric techniques are discussed around specific examples and applications.
2. [K] Koop,G., Analysis of Financial Data, 2006, Wiley. This book aims to teach financial econometrics to students whose primary interest is not in econometrics. These are the students who simply want to apply financial econometric techniques sensibly in the context of real-world empirical problems.
3. Hill, R.C., Griffiths, W.E., Lim, G.C., Principles of Econometrics, 4th (2011) or 5th (2018) editions, Wiley. This is an introductory book for undergraduate students in economics and finance, as well as for first-year graduate students in economics, finance, accounting, agricultural economics, marketing, public policy, sociology, law, and political science. It is assumed that students have taken courses in the principles of economics, and elementary statistics. Matrix algebra is not used, making it easier for aspiring empirical researchers; all calculus concepts are introduced and developed only in the appendices.

Panel data texts:

1. [BLT] Baltagi, B.H., Econometric Analysis of Panel Data, 3rd (2005) edition, Wiley. Some of the major features of this book are that it provides an up-to-date coverage of panel data techniques, especially for serial correlation, spatial correlation, heteroskedasticity,
   seemingly unrelated regressions, simultaneous equations, dynamic models, incomplete panels, limited dependent variables and nonstationary panels. IThnigs are keep simple (as opposed to a more advanced panel data text below), illustrating the basic ideas using the same notation for a diverse literature with heterogeneous notation. Many of the estimation and testing techniques are illustrated with data sets which are available on the publisher's web site. The book also cites and summarizes several empirical studies using panel data techniques, so that the reader can relate the econometric methods with the economic applications.
2. Wooldridge, J.M, Econometric Analysis of Cross Section and Panel Data, MIT Press. This book contains regorous derivations and extensions of panel models if you would like to focus on panel data.

Advanced texts focusing on economic and finance applications:

1. [V] Verbeek, M., A Guide to Modern Econometrics, 5th (2017) edition, Wiley. The goal of this book is to familiarize you with a wide range of topics in modern econometrics, focusing on what is important for doing and understanding empirical work. This means that the text is a guide to (rather than an overview of) alternative techniques. Consequently, it does not concentrate on the formulae behind each technique nor on formal proofs, but on the intuition behind the approaches and their practical relevance. The book covers a wide range of topics. In particular, attention is paid to cointegration, models with limited dependent variables and panel data models. More than 25 full-scale empirical illustrations are provided in separate sections and subsections, taken from fields like finance, international economics, consumer behaviour, environmental economics and macro-economics.

Elements of Machine Learning

1. [HTF] Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer. This book brings together many of the important new ideas in machine learning, and explains them in a statistical framework. While some mathematical details are needed, the book emphasizes the methods and their conceptual underpinnings rather than their theoretical properties. As a result, this book appeals not to statisticians but to researchers and practitioners in a wide variety of fields.
2. [P] de Prado, M.L., Advances in Financial Machine Learning, 2018, Wiley. This book bridges the proverbial divide that separates academia and the industry. This book will not advocate a theory merely because of its mathematical beauty, and will not propose a solution just because it appears to work. The goal of the book is to transmit the kind of knowledge that only comes from experience, formalized in a rigorous manner. Many firms will invest with off-the-shelfML algorithms, directly imported from academia or Silicon Valley. Beating the wisdom of the crowds, however, is harder than recognizing faces or driving cars. With this book you will learn how to solve some of the challenges that make finance a particularly difficult playground for machine learning, like backtest overfitting. Plus it has neat examples in Python.