1st Edition
Empirical Research in Accounting Tools and Methods
This textbook provides the foundation for a course that takes PhD students in empirical accounting research from the very basics of statistics, data analysis, and causal inference up to the point at which they conduct their own research. Starting with foundations in statistics, econometrics, causal inference, and institutional knowledge of accounting and finance, the book moves on to an in-depth coverage of the core papers in capital market research. The latter half of the book examines contemporary approaches to research design and empirical analysis, including natural experiments, instrumental variables, fixed effects, difference-in-differences, regression discontinuity design, propensity-score matching, and machine learning. Readers of the book will develop deep data analysis skills using modern tools. Extensive replication and simulation analysis is included throughout.
Key Features:
- Extensive coverage of empirical accounting research over more than 50 years.
- Integrated coverage of statistics and econometrics, institutional knowledge, and research design.
- Numerous replications and a dozen simulation analyses to immerse readers in papers and empirical analysis.
- All tables and figures in the book can be reproduced by readers using included code.
- Easy-to-use templates facilitate hands-on exercises and introduce reproduceable research concepts. (Solutions available to instructors.)
Preface
Part 1: Foundations
1. Introduction
2. Describing data
3. Regression fundamentals
4. Causal inference
5. Statistical inference
6. Financial statements: A first look
7. Linking databases
8. Financial statements: A second look
9. Importing data
Part 2: Capital Markets Research
10. FFJR
11. Ball and Brown (1968)
12. Beaver (1968)
13. Event studies
14. Post-earnings announcement drift
15. Accruals
16. Earnings management
Part 3: Causal Inference
17. Natural experiments
18. Causal mechanisms
19. Natural experiments revisited
20. Instrumental variables
21. Panel data
22. Regression discontinuity designs
Part 4: Additional Topics
23. Beyond OLS
24. Extreme values and sensitivity analysis
25. Matching
26. Prediction
Appendices
A. Linear algebra
B. SQL primer
C. Research computing overview
D. Running PostgreSQL
E. Making a parquet repository
References
Index
Biography
Ian D. Gow is a professor at the University of Melbourne, where he teaches several courses, including courses based on this book . Ian previously served on the faculties of Harvard Business School, Northwestern University, and Yale. Ian’s recent research focuses on causal inference and empirical methods. Ian has a PhD from Stanford, an MBA from Harvard and BCom and LLB degrees from the University of New South Wales.
Tongqing (Tony) Ding is a senior lecturer at the University of Melbourne, where he teaches courses on data analytics, financial statement analysis, and corporate reporting. Tony’s research focuses on corporate governance, financial reporting and disclosure, ESG, and data analytics. Tony has PhD and MS degrees from the University of Colorado and degrees from Shanghai Jiao Tong University.
