Panel Methods for Finance

Financial data are typically characterised by a time-series and cross-sectional dimension. Accordingly, econometric modelling in finance requires appropriate attention to these two or occasionally more than two dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications, including popular techniques such as Fama-MacBeth estimation, one-way, two-way and interactive fixed effects, clustered standard errors, instrumental variables, and difference-in-differences. Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications by Marno Verbeek offers the reader: Focus on panel methods where the time dimension is relatively small A clear and intuitive exposition, with a focus on implementation and practical relevance Concise presentation, with many references to financial applications and other sources Focus on techniques that are relevant for and popular in empirical work in finance and accounting Critical discussion of key assumptions, robustness, and other issues related to practical implementation

Auteur

Marno Verbeek , Erasmus University, The Netherlands

Résumé

"Panel Methods for Finance provides a practical and non-technical overview of econometric approaches using panel data in finance. It reviews several empirical examples from the financial literature applying these panel techniques and gives the reader useful suggestions on when and how to apply these tools in empirical finance. This should prove useful as a textbook or supplement for econometrics courses in finance."

Professor Badi H. Baltagi, Syracuse University, USA

"Marno Verbeek's new book includes a wide range of examples where panel data models are applied in finance, which is extremely useful to contextualise the models and to help readers understand where and how they can be used. The material is presented with great clarity, making it a pleasure to read. While the book is not overly technical, some mathematical detail is included for those who want it, with numerous references to further reading also provided. There is a broad coverage of models and estimation methods, including for panel specifications with limited dependent variables. The book is of just the right length and will be one which every empirical researcher in finance will want to have on their shelf".

Professor Chris Brooks, Bristol University, UK

Marno Verbeek provides an intuitive and relative non-technical outline of econometric techniques exploiting panel data with this book. *I will particularly enjoy using *Panel Methods for Finance as I currently work with a panel data set with a small time-series dimension. Marno provides complete explanations of different estimators and compares the benefits and drawbacks of using various econometric techniques. In addition, he discusses panel data from many different angles, from (un)common data issues, to the use of dynamic models and treatment effects. Furthermore, the examples of empirical research make the book intuitive and more accessible than other academic resources on panel data. Whether you are a student or a professor, this book is a must-have for anyone working with panel data. Knowing that I will work with more panel data sets in the future, this book will not leave my desk for a long time.

Eline ten Bosch

Marno Verbeek's book Panel Methods for Finance fills a gap in the offerings in textbooks. Panel data sets have both a time and cross-sectional dimension, and almost all data sets in finance with firms or securities are panels. Yet, there was no comprehensive guide for finance researchers in using panel estimations. I highly recommend the new book for advanced Masters and PhD students, and academics working in empirical finance. Verbeek provides a complete overview of panel data estimations, including fixed effects, instrumental variables, regression-discontinuity design, difference-in-differences, GMM, and discrete models. All approaches can be applied with Stata and are illustrated by published research in academic finance journals. The guide also provides many practical recommendations and discussions, such as *p-*hacking and dealing with outliers and missing data.

Abe de Jong - Professor of Finance, Monash University, Australia